Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks
暂无分享,去创建一个
[1] Yan Geng,et al. p63-expressing cells are the stem cells of developing prostate, bladder, and colorectal epithelia , 2013, Proceedings of the National Academy of Sciences.
[2] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Joseph K. Pickrell,et al. DNaseI sensitivity QTLs are a major determinant of human expression variation , 2011, Nature.
[4] A. Bird,et al. Methylation-Induced Repression— Belts, Braces, and Chromatin , 1999, Cell.
[5] J. Shendure,et al. A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.
[6] R. Mann,et al. The role of DNA shape in protein-DNA recognition , 2009, Nature.
[7] Jay Shendure,et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis , 2009, Nature Biotechnology.
[8] Michael Q. Zhang,et al. Computational prediction of methylation status in human genomic sequences. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[9] S. Rafii,et al. Splitting vessels: Keeping lymph apart from blood , 2003, Nature Medicine.
[10] Jeffrey W Pollard,et al. KLF15 negatively regulates estrogen-induced epithelial cell proliferation by inhibition of DNA replication licensing , 2012, Proceedings of the National Academy of Sciences.
[11] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[12] Wei Wang,et al. Predicting the Human Epigenome from DNA Motifs , 2014, Nature Methods.
[13] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[14] M. Daly,et al. Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants , 2014, Nature.
[15] Joseph B Hiatt,et al. Massively parallel functional dissection of mammalian enhancers in vivo , 2012, Nature Biotechnology.
[16] Simon C. Potter,et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis , 2011, Nature.
[17] S. Orkin,et al. Analysis of chromatin-state plasticity identifies cell-type–specific regulators of H3K27me3 patterns , 2014, Proceedings of the National Academy of Sciences.
[18] Matthew Slattery,et al. Absence of a simple code: how transcription factors read the genome. , 2014, Trends in biochemical sciences.
[19] Jun S. Liu,et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery , 2013 .
[20] Kate B. Cook,et al. Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity , 2014, Cell.
[21] Guido Sanguinetti,et al. Explorer Transcription factor binding predicts histone modifications in human cell lines , 2017 .
[22] Data production leads,et al. An integrated encyclopedia of DNA elements in the human genome , 2012 .
[23] D. Pe’er,et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.
[24] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[25] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[26] H. Bussemaker,et al. Regulatory element detection using correlation with expression , 2001, Nature Genetics.
[27] Kevin Y. Yip,et al. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer , 2014, Genome Biology.
[28] Myong-Hee Sung,et al. Transcription factor AP1 potentiates chromatin accessibility and glucocorticoid receptor binding. , 2011, Molecular cell.
[29] Ty C. Voss,et al. Dynamic regulation of transcriptional states by chromatin and transcription factors , 2013, Nature Reviews Genetics.
[30] Tatsunori B. Hashimoto,et al. Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape , 2014, Nature Biotechnology.
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] E. Segal,et al. In pursuit of design principles of regulatory sequences , 2014, Nature Reviews Genetics.
[33] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[34] Gary D. Stormo,et al. DNA binding sites: representation and discovery , 2000, Bioinform..
[35] Anne de Jong,et al. Adaptation of Hansenula polymorpha to methanol: a transcriptome analysis , 2010, BMC Genomics.
[36] Morteza Mohammad Noori,et al. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features , 2014, PLoS Comput. Biol..
[37] Michael A. Beer,et al. Predicting Gene Expression from Sequence , 2004, Cell.
[38] Mikhail Pachkov,et al. Modeling of epigenome dynamics identifies transcription factors that mediate Polycomb targeting , 2013, Genome research.
[39] Peggy Hall,et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..
[40] A. Visel,et al. Disruptions of Topological Chromatin Domains Cause Pathogenic Rewiring of Gene-Enhancer Interactions , 2015, Cell.
[41] Michael Q. Zhang,et al. CRISPR Inversion of CTCF Sites Alters Genome Topology and Enhancer/Promoter Function , 2015, Cell.
[42] Benjamin J. Strober,et al. A method to predict the impact of regulatory variants from DNA sequence , 2015, Nature Genetics.
[43] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] R. Rohs,et al. A widespread role of the motif environment in transcription factor binding across diverse protein families , 2015, Genome research.
[45] F. Collins,et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.
[46] Nathan C. Sheffield,et al. The accessible chromatin landscape of the human genome , 2012, Nature.
[47] Shane J. Neph,et al. Systematic Localization of Common Disease-Associated Variation in Regulatory DNA , 2012, Science.
[48] Michael Q. Zhang,et al. Integrative analysis of 111 reference human epigenomes , 2015, Nature.
[49] William Stafford Noble,et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors , 2012, Genome research.
[50] B. L,et al. The accessible chromatin landscape of the human genome , 2016 .
[51] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[52] K. Pollard,et al. Detection of nonneutral substitution rates on mammalian phylogenies. , 2010, Genome research.
[53] Xiang Zhang,et al. Text Understanding from Scratch , 2015, ArXiv.
[54] Bronwen L. Aken,et al. GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.
[55] Hang Li,et al. Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.
[56] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[57] Jo Lambert,et al. Genome-wide association analyses identify 13 new susceptibility loci for generalized vitiligo , 2012, Nature Genetics.
[58] Christina S. Leslie,et al. SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps , 2015, PLoS Comput. Biol..
[59] ENCODEConsortium,et al. An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.
[60] William Stafford Noble,et al. Quantifying similarity between motifs , 2007, Genome Biology.
[61] John M Cunningham,et al. Perturbed desmosomal cadherin expression in grainy head‐like 1‐null mice , 2008, The EMBO journal.