awesome-ml-epigenetics

References for ML in epigenetic study
https://github.com/jiajiexiao/awesome-ml-epigenetics

Last synced: about 7 hours ago
JSON representation

Papers
- Chromatin Accessibility
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning - A bootstrapping deep learning model to predict chromatin contacts between regulatory elements.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning - A bootstrapping deep learning model to predict chromatin contacts between regulatory elements.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
  - Effective gene expression prediction from sequence by integrating long-range interactions - Transformer-based model for chromatin accessibility prediction.
  - cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data - A probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data.
- Multi-omics Integration
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - MOFA+: A probabilistic framework for comprehensive integration of structured single-cell data - Framework for integrating multiple omics data types.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - MOFA+: A probabilistic framework for comprehensive integration of structured single-cell data - Framework for integrating multiple omics data types.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
  - SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks - Gradient-boost regression model for single-cell multiomic inference of enhancers and gene regulatory networks.
- Liquid Biopsy
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network - BERT-like model for early HCC detection.
  - Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring - MLE application in cfDNA tissue deconvolution.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA - LLM-based approach for cancer diagnosis using cfDNA end-motif profiles.
  - MethylGPT: a foundation model for the DNA methylome - A transformer-decoder-based LM pretrained on methylation microarray data.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA - LLM-based approach for cancer diagnosis using cfDNA end-motif profiles.
  - MethylGPT: a foundation model for the DNA methylome - A transformer-decoder-based LM pretrained on methylation microarray data.
  - Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA - Transformer's encoder + attention-based MIL for CRC and HCC detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers - BERT-like model on CpG sites.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network - BERT-like model for early HCC detection.
  - Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring - MLE application in cfDNA tissue deconvolution.
  - MethylBERT: A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution - 025-55920-z)) - BERT-like model pre-trained on human reference genome and adapted for methylation sequence profiles.
  - Bridging biological cfDNA features and machine learning approaches - 7) (2023) - Background in Biology for ML practitioners.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads - Hybrid sequence model (ConvNet+LSTM) for HCC detection with maximization of tumor fraction posterior probability.
  - CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data - Statistical model for read-level cancer detection from cfDNA.
  - Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA - Transformer's encoder + attention-based MIL for CRC and HCC detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers - BERT-like model on CpG sites.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - MethylBERT: A Transformer-based model for read-level DNA methylation pattern identification and tumour deconvolution - 025-55920-z)) - BERT-like model pre-trained on human reference genome and adapted for methylation sequence profiles.
  - Bridging biological cfDNA features and machine learning approaches - 7) (2023) - Background in Biology for ML practitioners.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads - Hybrid sequence model (ConvNet+LSTM) for HCC detection with maximization of tumor fraction posterior probability.
  - CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data - Statistical model for read-level cancer detection from cfDNA.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
  - A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing - MIL for MSI detection.
  - Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer - VAE-based model for early lung cancer detection using circulating RNAs.
  - Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs - Transformer's encoder that captures end-motif signatures for HCC.
  - Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis - CNN for lung cancer diagnosis.
  - The cell-free DNA methylome captures distinctions between localized and metastatic prostate tumors - Methylome analysis for prostate cancer staging.
  - Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis - Bayesian modeling for tumor fraction estimation.
- Novel Epigenetic Assays
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - DNA methylation detection: Bisulfite genomic sequencing analysis - Background of bisulfite sequencing. Also check [Bisulfite_sequencing](https://en.wikipedia.org/wiki/Bisulfite_sequencing) and [Reduced representation bisulfite sequencing](https://en.wikipedia.org/wiki/Reduced_representation_bisulfite_sequencing) on Wiki.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA - Enzymatic methyl sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - DNA methylation detection: Bisulfite genomic sequencing analysis - Background of bisulfite sequencing. Also check [Bisulfite_sequencing](https://en.wikipedia.org/wiki/Bisulfite_sequencing) and [Reduced representation bisulfite sequencing](https://en.wikipedia.org/wiki/Reduced_representation_bisulfite_sequencing) on Wiki.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - Enzymatic methyl sequencing detects DNA methylation at single-base resolution from picograms of DNA - Enzymatic methyl sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - DNA methylation detection: Bisulfite genomic sequencing analysis - Background of bisulfite sequencing. Also check [Bisulfite_sequencing](https://en.wikipedia.org/wiki/Bisulfite_sequencing) and [Reduced representation bisulfite sequencing](https://en.wikipedia.org/wiki/Reduced_representation_bisulfite_sequencing) on Wiki.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - DNA methylation detection: Bisulfite genomic sequencing analysis - Background of bisulfite sequencing. Also check [Bisulfite_sequencing](https://en.wikipedia.org/wiki/Bisulfite_sequencing) and [Reduced representation bisulfite sequencing](https://en.wikipedia.org/wiki/Reduced_representation_bisulfite_sequencing) on Wiki.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - DNA methylation detection: Bisulfite genomic sequencing analysis - Background of bisulfite sequencing. Also check [Bisulfite_sequencing](https://en.wikipedia.org/wiki/Bisulfite_sequencing) and [Reduced representation bisulfite sequencing](https://en.wikipedia.org/wiki/Reduced_representation_bisulfite_sequencing) on Wiki.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
  - Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals - Deep and less destructive assay than bisulfite sequencing.
  - scNMT-seq: Single-cell nucleosome, methylation and transcription sequencing - Single-cell nucleosome, methylation and transcription sequencing.
- DNA Methylation
  - A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles - Bayesian stats model for clustering/segment microarray data.
  - MethylNet: Deep learning for DNA methylation analysis - VAE for analyzing DNA methylation data.
  - DeepCpG: Accurate prediction of single-cell DNA methylation states using deep learning - CNN model for predicting single-cell DNA methylation states.
  - MethylNet: Deep learning for DNA methylation analysis - VAE for analyzing DNA methylation data.
  - DeepCpG: Accurate prediction of single-cell DNA methylation states using deep learning - CNN model for predicting single-cell DNA methylation states.
  - Epigenomic language models powered by Cerebras - BERT model pretrained on human genome and across 127 cell types with DNA sequence and paired epigenetic state inputs.
  - Detection of significantly differentially methylated regions in targeted bisulfite sequencing data - Stats model for identifying differentially methylated region (DMR) from microarray data (i.e. clustering/segment).
  - A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles - Bayesian stats model for clustering/segment microarray data.
  - A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles - Bayesian stats model for clustering/segment microarray data.
- Histone Modifications
  - DeepHistone: A deep learning approach to predicting histone modifications - CNN-based model for histone modification prediction.
  - DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications - Hybrid (attention + LSTM) deep learning model for gene expression prediction from histone modification.
  - DeepHistone: A deep learning approach to predicting histone modifications - CNN-based model for histone modification prediction.
  - DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications - Hybrid (attention + LSTM) deep learning model for gene expression prediction from histone modification.
Datasets
- Novel Epigenetic Assays
  - ENCODE - Encyclopedia of DNA Elements.
  - Roadmap Epigenomics - Comprehensive mapping of epigenomic states.
  - GEO - Gene Expression Omnibus, contains various epigenetics datasets.
  - EWAS Atlas - A comprehensive database for epigenome-wide association studies.
  - ClockBase - A curated methylation database for biological ages.
  - ENCODE - Encyclopedia of DNA Elements.

Categories

Papers 242 Datasets 6

Sub Categories

Liquid Biopsy 128 Novel Epigenetic Assays 49 Chromatin Accessibility 38 Multi-omics Integration 20 DNA Methylation 9 Histone Modifications 4