Using Fully Convolutional Network to Locate Transcription Factor Binding Sites Based on DNA Sequence and Conservation Information

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 20; no. 5; pp. 2690 - 2699
• Main Authors: Zhang, Qinhu, Xu, Youhong, Wang, Siguo, Wu, Yong, Ye, Yuannong, Yuan, Chang-An, Gribova, Valeriya, Filaretov, Vladimir Fedorovich, Huang, De-Shuang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Binding sites; Computer vision; conservation information; Conserved sequence; Convolutional codes; Decoding; Deep learning; Deoxyribonucleic acid; DNA; Feature extraction; Fully convolutional network; Gene expression; Gene sequencing; global average pooling layer; Mathematical models; Natural language processing; Nucleotide sequence; Nucleotides; Performance enhancement; refinement residual block; Regulation; Regulatory mechanisms (biology); transcription factor binding sites; Transcription factors

Transcription factors (TFs) play a part in gene expression. TFs can form complex gene expression regulation system by combining with DNA. Thereby, identifying the binding regions has become an indispensable step for understanding the regulatory mechanism of gene expression. Due to the great achievem...