Deep learning for DNase I hypersensitive sites identification

[1]  Xiaofeng Dai,et al.  pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines , 2018, Zeitschrift für Induktive Abstammungs- und Vererbungslehre.

[2]  Xiaolong Wang,et al.  Recurrent convolutional neural network for answer selection in community question answering , 2018, Neurocomputing.

[3]  Lei Wang,et al.  LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks , 2018, Bioinform..

[4]  A. Ben-Hur,et al.  Exploring the relationship between intron retention and chromatin accessibility in plants , 2018, BMC Genomics.

[5]  K. Zhao,et al.  Genome-wide mapping of DNase I hypersensitive sites in rare cell populations using single-cell DNase sequencing , 2017, Nature Protocols.

[6]  Zhangxin Chen,et al.  ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network , 2017, Molecules.

[7]  Muhammad Kabir,et al.  Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition , 2017 .

[8]  Chao Ren,et al.  BiRen: predicting enhancers with a deep‐learning‐based model using the DNA sequence alone , 2017, Bioinform..

[9]  Yann Dauphin,et al.  Language Modeling with Gated Convolutional Networks , 2016, ICML.

[10]  Ren Long,et al.  iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework , 2016, Bioinform..

[11]  Beilun Wang,et al.  Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks , 2016, PSB.

[12]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[13]  Tomáš Vinař,et al.  DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads , 2016, PloS one.

[14]  Yi Li,et al.  Instance-Sensitive Fully Convolutional Networks , 2016, ECCV.

[15]  Hongkai Ji,et al.  Genome-wide prediction of DNase I hypersensitivity using gene expression , 2016, bioRxiv.

[16]  Xiaohui S. Xie,et al.  DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.

[17]  Jianyang Zeng,et al.  A deep learning framework for modeling structural features of RNA-binding protein targets , 2015, Nucleic acids research.

[18]  David R. Kelley,et al.  Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.

[19]  Tao Zhang,et al.  PlantDHS: a database for DNase I hypersensitive sites in plants , 2015, Nucleic Acids Res..

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

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

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

[23]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[24]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Hong-Bin Shen,et al.  Predicting pupylation sites in prokaryotic proteins using pseudo-amino acid composition and extreme learning machine , 2014, Neurocomputing.

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

[27]  Zhengwei Zhu,et al.  CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..

[28]  Nathan C. Sheffield,et al.  The accessible chromatin landscape of the human genome , 2012, Nature.

[29]  Tommi S. Jaakkola,et al.  Lineage-based identification of cellular states and expression programs , 2012, Bioinform..

[30]  M. Seki,et al.  ARTADE2DB: Improved Statistical Inferences for Arabidopsis Gene Functions and Structure Predictions by Dynamic Structure-Based Dynamic Expression (DSDE) Analyses , 2011, Plant & cell physiology.

[31]  K. Shinozaki,et al.  Genome-wide analysis of endogenous abscisic acid-mediated transcription in dry and imbibed seeds of Arabidopsis using tiling arrays. , 2010, The Plant journal : for cell and molecular biology.

[32]  Xiangfeng Wang,et al.  Genome-wide profiling of histone H3 lysine 9 acetylation and dimethylation in Arabidopsis reveals correlation between multiple histone marks and gene expression , 2010, Plant Molecular Biology.

[33]  M. Pellegrini,et al.  Genome-wide analysis of mono-, di- and trimethylation of histone H3 lysine 4 in Arabidopsis thaliana , 2009, Genome Biology.

[34]  S. Henikoff,et al.  Histone H2A.Z and DNA methylation are mutually antagonistic chromatin marks , 2008, Nature.

[35]  Z. Weng,et al.  High-Resolution Mapping and Characterization of Open Chromatin across the Genome , 2008, Cell.

[36]  V. Gaudin,et al.  The Arabidopsis LHP1 protein colocalizes with histone H3 Lys27 trimethylation , 2007, Nature Structural &Molecular Biology.

[37]  A. Mortazavi,et al.  Genome-Wide Mapping of in Vivo Protein-DNA Interactions , 2007, Science.

[38]  M. Daly,et al.  Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). , 2005, Genome research.

[39]  William Stafford Noble,et al.  Predicting the in vivo signature of human gene regulatory sequence , 2005, ISMB.

[40]  Michael Black,et al.  Role of transposable elements in heterochromatin and epigenetic control , 2004, Nature.

[41]  G. Felsenfeld,et al.  Chromatin as an essential part of the transcriptional mechanim , 1992, Nature.

[42]  Sarah C. R. Elgin,et al.  The chromatin structure of specific genes: I. Evidence for higher order domains of defined DNA sequence , 1979, Cell.

[43]  T. Maniatis,et al.  Structure of the λ Operators , 1973, Nature.

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

[45]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[46]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.