Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-biomedical-machine-learning
https://github.com/DenseAI/awesome-biomedical-machine-learning
- 1. 生物 Biology
- 2. 基因 Genomics
- 2.1 基因序列分析与预测 Gene sequence analysis and prediction
- 2.2 泛癌症分析 Pan-cancer analysis
- 2.3 单细胞 Single-Cell
- 2.4 表观基因组学 Epigenomes
- Helmholtz Zentrum München, German Research Center for Environmental Health, Theis lab
- 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
- Learning causality in genomics
- DANN: a deep learning approach for annotating the pathogenicity of genetic variants - 763.
- *** 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.
- DeepWAS: Directly integrating regulatory information into GWAS using machine learning
- Graph attention networks
- BioBombe: Sequentially compressed gene expression features enhances biological signatures
- DANN
- DeepBind
- A unified approach to interpreting model predictions - 4774.
- shap: A game theoretic approach to explain the output of any machine learning model
- T2DKP Datasets
- 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
- Integrating multi-omics data with deep learning for predicting cancer prognosis
- Cancer classification of single-cell gene expression data by neural network - 1366.
- Deep learning based tumor type classification using gene expression data - 96.
- 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 tumor type classification using gene expression data[C - 96.
- Deep learning–based multi-omics integration robustly predicts survival in liver cancer[J - 1259.
- 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
- tcga: Microbial analysis in TCGA data
- cancer-data: TCGA data acquisition and processing for Project Cognoma
- DeepMicro: Deep representation learning for disease prediction based on microbiome data
- overall_survival_nsclc: bimodal DNN for NSCLC patient overall survival prediction
- DeepSVR
- cancer_subtyping
- *** DL-based-Tumor-Classification: Deep Learning Based Tumor Type Classification Using Gene Expression Data
- SurvivalNet: SurvivalNet is a package for building survival analysis models using deep learning.
- DeepCC: a deep learning-based framework for cancer classification
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of random forests and a deep neural network
- 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
- *** RNA sequencing: the teenage years - 656.
- PDF - RNA%20sequencing%20the%20teenage%20years/)
- *** Current best practices in single‐cell RNA‐seq analysis: a tutorial
- 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
- *** 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
- 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
- DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
- Unsupervised generative and graph representation learning for modelling cell differentiation - Terré, Ana Cvejic, Pietro Liò. bioRxiv 801605; doi: https://doi.org/10.1101/801605
- A comparison of automatic cell identification methods for single-cell RNA sequencing data - 019-1795-z
- scRNAseq_Benchmark
- 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
- DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data - 019-1837-6
- 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
- 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.
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling - 019-0529-1
- PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells - 019-1663-x
- 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
- *** 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.
- *** DCA: Deep count autoencoder for denoising scRNA-seq data
- scDeepCluster for Single Cell RNA-seq data
- cellassign: Automated, probabilistic assignment of cell types in scRNA-seq data
- scVI: scDeep generative modeling for single-cell omics data
- scVAE: Variational auto-encoders for single-cell gene expression data
- KPNN: Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
- deepimpute: An accurate and efficient deep learning method for single-cell RNA-seq data imputation
- DiffVAE: Unsupervised generative and graph neural methods for modelling cell differentiation
- scScope
- CNNC: covolutional neural network based coexpression analysis
- deepimpute: An accurate and efficient deep learning method for single-cell RNA-seq data imputation
- digitalDLSorter: A pipeline to generate a Deep Nerual Network cell type deconvolution model for bulk RNASeq samples from single cell experiment data
- Deep learning approaches for single cell data
- awesome-single-cell
- singlecelldb: Deep learning approaches for single cell data
- scHCL: A tool defines cell types in human based on single-cell digital expression
- SCINET: Single-Cell Imputation and NETwork inference
- paga: Mapping out the coarse-grained connectivity structures of complex manifolds.
- 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
- 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
- Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype - i547.
- GraBLD: an R based software package that makes polygenic traits prediction using gradient boosted and LD adjusted gene score weights
- ldpred: a Python based software package that adjusts GWAS summary statistics for the effects of linkage disequilibrium (LD)
- AnnoPred: Genetic risk prediction integrating LD and functional annotations
- The accessible chromatin landscape of the human genome
- CCKS 2017 电子病历命名实体识别
- CCKS 2018 面向中文电子病历的命名实体识别
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning - 1567.
- Deep learning-based survival prediction for multiple cancer types using histopathology images[J
- DeepPATH: The DeepPATH framework gathers the codes that have been used to study the use of a deep learning architecture (inception v3 from Google) to classify Lung cancer images.
- Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy - e180926.
Programming Languages
Keywords
deep-learning
5
gene-expression
3
bioinformatics
3
cancer
2
python
2
tcga
2
single-cell
2
machine-learning
2
deep-neural-networks
1
microarray
1
non-small-cell-lung-cancer
1
overall-survival-prediction
1
prognostic-biomarkers
1
autoencoders
1
rna-seq
1
analysis
1
analysis-pipeline
1
atac-seq
1
awesome-list
1
clinical-data
1
biomarkers
1
bimodal-multicast
1
genomics
1
unsupervised-learning
1
single-cell-rna-seq
1
xena-browser
1
xena
1
mutation
1
dataphilly
1
data-acquisition
1
shapley
1
shap
1
interpretability
1
gradient-boosting
1
explainability
1
network
1
msigdb
1
hetnet
1
gene-sets
1
compression
1
biobombe
1
autoencoder
1
tensorflow
1
self-attention
1
neural-networks
1
graph-attention-networks
1
attention-mechanism
1
scrna-seq-data
1
rna-seq-experiments
1
rna-seq-data
1