Structure-based prediction of protein– peptide binding regions using Random Forest-Reference-Cited by-同舟云学术

Structure-based prediction of protein– peptide binding regions using Random Forest

Published:2017-09-26 Issue:3 Volume:34 Page:477-484
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Taherzadeh Ghazaleh¹,Zhou Yaoqi¹²,Liew Alan Wee-Chung¹,Yang Yuedong¹²³^ORCID

Affiliation:

1. School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia

2. Institute for Glycomics, Griffith University, Parklands Drive, Southport, QLD, Australia

3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

Funder

NHMRC

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/article-pdf/34/3/477/25117086/btx614.pdf

Reference66 articles.

1. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs;Altschul;Nucleic Acids Res,1997

2. Small-world communication of residues and significance for protein dynamics;Atilgan;Biophys. J,2004

3. Predicting protein-ligand and protein-peptide interfaces;Bertolazzi;Eur. Phys. J. Plus,2014

4. Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking;Blaszczyk;Methods,2016

5. Random forests;Breiman;Mach. Learn,2001

Cited by 68 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism;Analytical Biochemistry;2024-11

2. DeepPepPI: A deep cross-dependent framework with information sharing mechanism for predicting plant peptide-protein interactions;Expert Systems with Applications;2024-10

3. DP-site: A dual deep learning-based method for protein-peptide interaction site prediction;Methods;2024-09

4. DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning;BMC Bioinformatics;2024-07-31

5. ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites;Journal of Molecular Graphics and Modelling;2024-07