R
Rong Liu
Researcher at Huazhong Agricultural University
Publications - 21
Citations - 363
Rong Liu is an academic researcher from Huazhong Agricultural University. The author has contributed to research in topics: Protein structure prediction & Heme binding. The author has an hindex of 11, co-authored 21 publications receiving 301 citations. Previous affiliations of Rong Liu include Huazhong University of Science and Technology & University of South Carolina.
Papers
More filters
Journal ArticleDOI
Computational Prediction of Heme-Binding Residues by Exploiting Residue Interaction Network
Rong Liu,Jianjun Hu +1 more
TL;DR: Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network, which led to reliable performance improvement as HemeNet was applied to predicting the binding residues of proteins in the heme
Journal ArticleDOI
DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches
Rong Liu,Jianjun Hu +1 more
TL;DR: It is shown that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures, and significantly outperformed the state‐of‐art algorithms by around 10% in terms of Matthews's correlation coefficient.
Journal ArticleDOI
HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information
Rong Liu,Jianjun Hu +1 more
TL;DR: HemeBIND is the first specialized algorithm used to predict binding residues in protein structures for heme ligands by integrating structural and sequence information and demonstrates remarkably better performance than the individual classifier alone.
Journal ArticleDOI
Minimalist ensemble algorithms for genome-wide protein localization prediction
TL;DR: A novel method for rational design of minimalist ensemble algorithms using feature selection and classifiers based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms.
Journal ArticleDOI
Identifying protein-protein interaction sites in transient complexes with temperature factor, sequence profile and accessible surface area.
TL;DR: The results of threefold cross-validation on the nonredundant dataset show that when B-factor was used as an additional feature, the prediction performance can be improved significantly and can be applied to complement experimental techniques in studying transient protein–protein interactions.