(Q92685904)

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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

scientific article published on 26 March 2019

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1 May 2020

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data (English)

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1 May 2020

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Daniel Quang

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1 May 2020

Xiaohui Xie

2

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1 May 2020

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26 March 2019

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1 May 2020

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1 May 2020

166

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1 May 2020

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1 May 2020

Genome-wide mapping of in vivo protein-DNA interactions

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Global mapping of protein-DNA interactions in vivo by digital genomic footprinting

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High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells

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msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

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EXTREME: an online EM algorithm for motif discovery

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Deep learning of the tissue-regulated splicing code

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DANN: a deep learning approach for annotating the pathogenicity of genetic variants

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

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

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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

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

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Framewise phoneme classification with bidirectional LSTM and other neural network architectures.

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DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

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BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.

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Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

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7 January 2021

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Design and analysis of ChIP-seq experiments for DNA-binding proteins

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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

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FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin

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Identifiers

10.1016/J.YMETH.2019.03.020

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Europe PubMed Central

PubMed publication ID

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:30922998%20AND%20SRC:MED&resulttype=core&format=json

1 May 2020

PubMed publication ID

1 reference

Europe PubMed Central

PubMed publication ID

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:30922998%20AND%20SRC:MED&resulttype=core&format=json

1 May 2020

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