MethylNet: an automated and modular deep learning approach for DNA methylation analysis

[1]  S. Horvath,et al.  DNA methylation aging clocks: challenges and recommendations , 2019, Genome Biology.

[2]  Feng Luo,et al.  DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning. , 2019, Bioinformatics.

[3]  S. Li,et al.  DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning , 2019, Genes.

[4]  Joshua J. Levy,et al.  PyMethylProcess - convenient high-throughput preprocessing workflow for DNA methylation data , 2019, Bioinform..

[5]  Jack A. Taylor,et al.  Blood DNA methylation and breast cancer: A prospective case-cohort analysis in the Sister Study. , 2019, Journal of the National Cancer Institute.

[6]  Ji Wan,et al.  Clustering single-cell RNA-seq data with a model-based deep learning approach , 2019, Nature Machine Intelligence.

[7]  Andreas Joseph,et al.  Parametric inference with universal function approximators , 2019, SSRN Electronic Journal.

[8]  Jack A. Taylor,et al.  Methylation-based biological age and breast cancer risk. , 2019, Journal of the National Cancer Institute.

[9]  Janet M. Thornton,et al.  Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1 , 2019, Genome Biology.

[10]  Olgica Milenkovic,et al.  E2M: A Deep Learning Framework for Associating Combinatorial Methylation Patterns with Gene Expression , 2019, bioRxiv.

[11]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[12]  C. Greene,et al.  Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics , 2018, PSB.

[13]  Yadong Wang,et al.  Exploring DNA Methylation Data of Lung Cancer Samples with Variational Autoencoders , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[14]  Luigi Ferrucci,et al.  A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study , 2018, PLoS medicine.

[15]  Michael I. Jordan,et al.  Deep Generative Modeling for Single-cell Transcriptomics , 2018, Nature Methods.

[16]  Carly A. Bobak,et al.  An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer , 2018 .

[17]  Yang Wang,et al.  A deep neural network based regression model for triglyceride concentrations prediction using epigenome-wide DNA methylation profiles , 2018, BMC Proceedings.

[18]  Hong Zheng,et al.  A deep learning framework for imputing missing values in genomic data , 2018, bioRxiv.

[19]  M. Pellegrini,et al.  Human Epigenetic Aging is Logarithmic with Time across the Entire LifeSpan , 2018, bioRxiv.

[20]  B. Christensen,et al.  Tracing human stem cell lineage during development using DNA methylation , 2018, Genome research.

[21]  Zhanyu Ma,et al.  Deep Neural Network for Analysis of DNA Methylation Data , 2018, 1808.01359.

[22]  Rondi A. Butler,et al.  An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray , 2018, Genome Biology.

[23]  I. Férnandez-Cadenas,et al.  Biological Age is a predictor of mortality in Ischemic Stroke , 2018, Scientific Reports.

[24]  Kirthevasan Kandasamy,et al.  Neural Architecture Search with Bayesian Optimisation and Optimal Transport , 2018, NeurIPS.

[25]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[26]  Brock C. Christensen,et al.  A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation , 2018, BIOINFORMATICS.

[27]  M. Krane,et al.  DNA methylation signatures follow preformed chromatin compartments in cardiac myocytes , 2017, Nature Communications.

[28]  A. Frigessi,et al.  DNA methylation at enhancers identifies distinct breast cancer lineages , 2017, Nature Communications.

[29]  Manolis Kellis,et al.  Chromatin-state discovery and genome annotation with ChromHMM , 2017, Nature Protocols.

[30]  Casey S. Greene,et al.  Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders , 2017, bioRxiv.

[31]  Bradley J. Erickson,et al.  Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status , 2017, Journal of Digital Imaging.

[32]  B. Christensen,et al.  Cell-type deconvolution from DNA methylation: a review of recent applications , 2017, Human molecular genetics.

[33]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[34]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[35]  O. Stegle,et al.  DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2017, Genome Biology.

[36]  Shijie C. Zheng,et al.  A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies , 2017, bioRxiv.

[37]  Christopher Burgess,et al.  beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.

[38]  D. Gifford,et al.  Predicting the impact of non-coding variants on DNA methylation , 2016, bioRxiv.

[39]  Paolo Vineis,et al.  Epigenetic Signatures of Cigarette Smoking , 2016, Circulation. Cardiovascular genetics.

[40]  John Chilton,et al.  Common Workflow Language, v1.0 , 2016 .

[41]  M. Ringnér,et al.  An integrated genomics analysis of epigenetic subtypes in human breast tumors links DNA methylation patterns to chromatin states in normal mammary cells , 2016, Breast Cancer Research.

[42]  C. Marsit,et al.  Reference-free deconvolution of DNA methylation data and mediation by cell composition effects , 2016, bioRxiv.

[43]  Dong Xu,et al.  Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks , 2016, Scientific Reports.

[44]  Jovana Maksimovic,et al.  missMethyl: an R package for analyzing data from Illumina's HumanMethylation450 platform , 2016, Bioinform..

[45]  M. Esteller,et al.  Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences , 2015, Epigenomics.

[46]  Nathan C. Sheffield,et al.  LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor , 2015, Bioinform..

[47]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[48]  Michael R. Crusoe,et al.  Common Workflow Language , 2015 .

[49]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[50]  Carl Boettiger,et al.  An introduction to Docker for reproducible research , 2014, OPSR.

[51]  S. Horvath.  DNA methylation age of human tissues and cell types , 2013, Genome Biology.

[52]  Aaron Golden,et al.  Gene-set analysis is severely biased when applied to genome-wide methylation data , 2013, Bioinform..

[53]  Ulf Gyllensten,et al.  Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan , 2013, PloS one.

[54]  T. Ideker,et al.  Genome-wide methylation profiles reveal quantitative views of human aging rates. , 2013, Molecular cell.

[55]  Martin J. Aryee,et al.  Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in Rheumatoid Arthritis , 2013, Nature Biotechnology.

[56]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[57]  Devin C. Koestler,et al.  DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.

[58]  B. Christensen,et al.  Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context , 2009, PLoS genetics.

[59]  Margaret R. Karagas,et al.  Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions , 2008, BMC Bioinformatics.