CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

Journal: BMC genomics

PMID: 40102719

Abstract

BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent years, convolutional neural networks (CNNs) have succeeded in TF-DNA binding prediction, but existing DL methods' accuracy needs to be improved and convolution function in TF-DNA binding prediction should be further explored.

Authors

Shuangquan Zhang

Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Anjun Ma

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Xuping Xie
Zhichao Lian
Yan Wang

College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.

Keywords

Algorithms Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Convolutional Neural Networks DNA Neural Networks, Computer Protein Binding Software Transcription Factors

External Resources

View on PubMed Access via DOI PubMed (40102719)

CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals