Locating transcription factor binding sites by fully convolutional neural network

Predicting in-vitro Transcription Factor Binding Sites Using DNA Sequence + Shape

(2019) Qinhu Zhang et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites

(2018) Qinhu Zhang et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

SMiLE-seq identifies binding motifs of single and dimeric transcription factors

(2017) Alina Isakova et al.   NATURE METHODS

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis

(2017) Ivan V Kulakovskiy et al.   NUCLEIC ACIDS RESEARCH

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

(2016) Daniel Quang et al.   NUCLEIC ACIDS RESEARCH

DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo

(2016) Anthony Mathelier et al.   Cell Systems

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Predicting effects of noncoding variants with deep learning–based sequence model

(2015) Jian Zhou et al.   NATURE METHODS

Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors

(2012) J. Wang et al.   GENOME RESEARCH

ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions

(2012) Terrence S. Furey   NATURE REVIEWS GENETICS

DREME: motif discovery in transcription factor ChIP-seq data

(2011) Timothy L. Bailey   BIOINFORMATICS

MEME-ChIP: motif analysis of large DNA datasets

(2011) Philip Machanick et al.   BIOINFORMATICS

A census of human transcription factors: function, expression and evolution

(2009) Juan M. Vaquerizas et al.   NATURE REVIEWS GENETICS