Elucidation of DNA methylation on N6-adenine with deep learning

[1]  C. Niehrs,et al.  The origin of genomic N6-methyl-deoxyadenosine in mammalian cells , 2020, Nature Chemical Biology.

[2]  Q. Xia,et al.  Epigenetic Methylations on N6-Adenine and N6-Adenosine with the same Input but Different Output , 2019, International journal of molecular sciences.

[3]  Chuan He,et al.  6mA-DNA-binding factor Jumu controls maternal-to-zygotic transition upstream of Zelda , 2019, Nature Communications.

[4]  Fan Liang,et al.  DNA N6-Adenine Methylation in Arabidopsis thaliana. , 2018, Developmental cell.

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

[6]  Chuan He,et al.  Abundant DNA 6mA methylation during early embryogenesis of zebrafish and pig , 2016, Nature Communications.

[7]  James A. Swenberg,et al.  DNA methylation on N6-adenine in mammalian embryonic stem cells , 2016, Nature.

[8]  Yang Shi,et al.  DNA N6-methyladenine: a new epigenetic mark in eukaryotes? , 2015, Nature Reviews Molecular Cell Biology.

[9]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[10]  B. Frey,et al.  Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.

[11]  Benjamin J. Strober,et al.  A method to predict the impact of regulatory variants from DNA sequence , 2015, Nature Genetics.

[12]  L. Doré,et al.  N 6-Methyldeoxyadenosine Marks Active Transcription Start Sites in Chlamydomonas , 2015, Cell.

[13]  Shunmin He,et al.  N6-Methyladenine DNA Modification in Drosophila , 2015, Cell.

[14]  M. Esteller,et al.  An Adenine Code for DNA: A Second Life for N6-Methyladenine , 2015, Cell.

[15]  L. Aravind,et al.  DNA Methylation on N6-Adenine in C. elegans , 2015, Cell.

[16]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

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

[19]  Yong Zhang,et al.  Identifying ChIP-seq enrichment using MACS , 2012, Nature Protocols.

[20]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[21]  Guoli Ji,et al.  A classification-based prediction model of messenger RNA polyadenylation sites. , 2010, Journal of theoretical biology.

[22]  C. Glass,et al.  Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. , 2010, Molecular cell.

[23]  M. Pellegrini,et al.  Conservation and divergence of methylation patterning in plants and animals , 2010, Proceedings of the National Academy of Sciences.

[24]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[25]  P. D’haeseleer What are DNA sequence motifs? , 2006, Nature Biotechnology.

[26]  C. Elkan,et al.  Unsupervised learning of multiple motifs in biopolymers using expectation maximization , 1995, Machine Learning.

[27]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[28]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[29]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[30]  M. Marinus,et al.  The great GATC: DNA methylation in E. coli. , 1989, Trends in genetics : TIG.

[31]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .