How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets-Reference-Cited by-同舟云学术

How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets

Published:2023-03-01 Issue:2 Volume:20 Page:1594-1599
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Quan Lijun¹^ORCID,Chu Xiaomin¹,Sun Xiaoyu²,Wu Tingfang¹^ORCID,Lyu Qiang¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China

2. Zhengzhou Xinda Institute of Advanced Technology, Zhengzhou, Henan, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province Youth Fund

A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Collaborative Innovation Center of Novel Software Technology and Industrialization

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/10091711/09763317.pdf?arnumber=9763317

Reference70 articles.

1. Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

2. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features

3. Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix

4. SIFT missense predictions for genomes

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

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