Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequenc...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics 2022-03, Vol.19 (2), p.998-1008
Hauptverfasser:	Quan, Lijun, Sun, Xiaoyu, Wu, Jian, Mei, Jie, Huang, Liqun, He, Ruji, Nie, Liangpeng, Chen, Yu, Lyu, Qiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Abnormalities Base Sequence Binding Binding Sites - genetics Bioinformatics ChIP-seq Chromatin Immunoprecipitation Sequencing Data models Datasets Deep learning Deoxyribonucleic acid DNA Experimental data Feature extraction Gene expression Gene sequencing Genomics Nucleotide sequence Protein Binding Representations Retinoid X receptors semantic features Semantics Transcription factor binding specificities Transcription factors Transcription Factors - genetics Transcription Factors - metabolism
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