Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families-Reference-Cited by-同舟云学术

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Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families

Published:2020-01-16 Issue:3 Volume:60 Page:1876-1883
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Wang Zongyu¹,He Wenying¹,Tang Jijun¹²³,Guo Fei¹^ORCID

Affiliation:

1. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

2. Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, P. R. China

3. Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29208, United States

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b01012

Reference68 articles.

1. Direct GR Binding Sites Potentiate Clusters of TF Binding across the Human Genome

2. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments

3. Stability selection for regression-based models of transcription factor-DNA binding specificity

4. 14 Quantitative modeling of transcription factor binding specificities using DNA shape

5. Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights

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1. PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2024-05

2. MachineTFBS: Motif-based method to predict transcription factor binding sites with first-best models from machine learning library;Biochemical Engineering Journal;2023-09

3. Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2023-03-01

4. Disease-Associated Non-Coding Variants Alter NKX2-5 DNA-Binding Affinity;2022-12-02

5. dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost;Methods;2022-08

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