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awesome-biomedical-machine-learning
https://github.com/DenseAI/awesome-biomedical-machine-learning
Last synced: 2 days ago
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目录
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2. 基因 Genomics
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- 重磅综述：三万字长文读懂单细胞RNA测序分析的最佳实践教程 （原理、代码和评述）
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- The accessible chromatin landscape of the human genome
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- *** RNA sequencing: the teenage years - 656.
- *** Current best practices in single‐cell RNA‐seq analysis: a tutorial
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- Kellis Lab at MIT Computer Science and Broad Institute
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Computational Systems Biology: Deep Learning in the Life Sciences
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- The Biology of Human Diseases, as Revealed Through Genomics
- *** Predicting effects of noncoding variants with deep learning–based sequence model - 934.
- *** Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
- DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
- Graph Neural Networks: A Review of Methods and Applications
- A Comprehensive Survey on Graph Neural Networks
- *** A general framework for estimating the relative pathogenicity of human genetic variants
- *** CADD: predicting the deleteriousness of variants throughout the human genome - D894.
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Gene co-expression analysis for functional classification and gene–disease predictions[J - 592.
- *** FTO obesity variant circuitry and adipocyte browning in humans. - 907.
- DANN
- T2DKP Datasets
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Integrating multi-omics data with deep learning for predicting cancer prognosis
- Cancer classification of single-cell gene expression data by neural network - 1366.
- Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- *** Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer - 1947. e5.
- Deep learning–based multi-omics integration robustly predicts survival in liver cancer[J - 1259.
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- CancerSEA: a cancer single-cell state atlas - D908.
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated to human diseases
- PDF - RNA%20sequencing%20the%20teenage%20years/)
- 重磅综述：三万字长文读懂单细胞RNA测序分析的最佳实践教程 （原理、代码和评述）
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data - 019-1837-6
- Unsupervised generative and graph representation learning for modelling cell differentiation - Terré, Ana Cvejic, Pietro Liò. bioRxiv 801605; doi: https://doi.org/10.1101/801605
- Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning - 314.
- Deep learning for inferring gene relationships from single-cell expression data - Joseph Z. Proceedings of the National Academy of Sciences, 2019, 116(52): 27151-27158.
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution - Velderrain J, Kellis M. Cell Systems, 2019, 9(6): 559-568. e4.
- PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells - 019-1663-x
- A reference map of the human binary protein interactome - 020-2188-x
- *** Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection - 8.
- Comprehensive integration of single-cell data - 8) Stuart T, Butler A, Hoffman P, et al. Cell, 2019, 177(7): 1888-1902. e21.
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection
- Towards clinical utility of polygenic risk scores
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Machine learning SNP based prediction for precision medicine
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- DeepBind
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis
- Using neural networks for reducing the dimensions of single-cell RNA-Seq data - e156.
- DigitalDLSorter: Deep-Learning on scRNA-Seq to deconvolute gene expression data - Cabo F. Frontiers in genetics, 2019, 10: 978.
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- singlecelldb: Deep learning approaches for single cell data
- CellMarker: a manually curated resource of cell markers in human and mouse
- HuRI: The Human Reference Protein Interactome Mapping Project
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Learning causality in genomics
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- *** Deep learning based tumor type classification using gene expression data[C - 96.
- CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms[J - e62.
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- A comparison of automatic cell identification methods for single-cell RNA sequencing data - 019-1795-z
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype - i547.
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- The evolutionary history of 2,658 cancers - 128.
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Cancer classification of single-cell gene expression data by neural network - 1366.
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** Construction of a human cell landscape at single-cell level - 020-2157-4
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The personal and clinical utility of polygenic risk scores - 018-0018-x
- The accessible chromatin landscape of the human genome
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- A reference map of the human binary protein interactome - 020-2188-x
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- The accessible chromatin landscape of the human genome
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- DANN: a deep learning approach for annotating the pathogenicity of genetic variants - 763.
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- *** RNA sequencing: the teenage years - 656.
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- Pan-cancer analysis of whole genomes
- The repertoire of mutational signatures in human cancer - 101.
- The evolutionary history of 2,658 cancers - 128.
- Patterns of somatic structural variation in human cancer genomes - 019-1913-9
- Genomic basis for RNA alterations in cancer - 020-1970-0
- Analyses of non-coding somatic drivers in 2,658 cancer whole genomes - 020-1965-x
- Comprehensive molecular characterization of mitochondrial genomes in human cancers - 019-0557-x
- Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer - 019-0564-y
- *** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns - 019-13825-8
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives - 124.
- Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
- *** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. - 019-0420-0
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning - 020-60740-w
- Deep learning: new computational modelling techniques for genomics - 019-0122-6
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network
- *** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- *** RNA sequencing: the teenage years - 656.
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach - 020-2095-1
- *** Identification of 12 cancer types through genome deep learning - 019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data - 020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning - 020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data - 018-0257-y
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders - 019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine - Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction - 019-0364-z
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models - 017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. - 019-0157-8
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. - 019-0251-6
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly - 019-14079-0
- 重磅综述：三万字长文读懂单细胞RNA测序分析的最佳实践教程 （原理、代码和评述）
- *** Single-cell RNA-seq denoising using a deep count autoencoder - 018-07931-2
- Clustering single-cell RNA-seq data with a model-based deep learning approach - 019-0037-0
- Integrative analysis of 111 reference human epigenomes
- An integrated encyclopedia of DNA elements in the human genome
- A machine-learning heuristic to improve gene score prediction of polygenic traits - 017-13056-1
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps - 018-0241-6
- Analysis of polygenic risk score usage and performance in diverse human populations - 019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers - 020-0800-0
- The accessible chromatin landscape of the human genome
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- Emerging deep learning methods for single-cell RNA-seq data analysis - 019-0189-2
- Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) - 020-0262-0
- Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data - 019-56911-z
- Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks - 019-14018-z
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3 生物医学 Biomedical Science
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- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
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- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
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- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
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- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
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