Chunhua Li

Orcid: 0000-0002-0895-3506

Affiliations:
  • Beijing University of Technology, Faculty of Environmental and Life Sciences, College of Chemistry and Life Science, China


According to our database1, Chunhua Li authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

2019
2020
2021
2022
2023
2024
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Legend:

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Links

Online presence:

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Bibliography

2024
ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.
J. Chem. Inf. Model., 2024

Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model.
J. Chem. Inf. Model., 2024

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.
J. Chem. Inf. Model., 2024

2023
Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52.
J. Chem. Inf. Model., September, 2023

emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model.
Briefings Bioinform., July, 2023

Identification of metal ion-binding sites in RNA structures using deep learning method.
Briefings Bioinform., March, 2023

2022
Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method.
J. Chem. Inf. Model., 2022

An ensemble approach to predict binding hotspots in protein-RNA interactions based on SMOTE data balancing and Random Grouping feature selection strategies.
Bioinform., 2022

Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation.
Briefings Bioinform., 2022

2021
Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models.
J. Chem. Inf. Model., 2021

aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks.
Bioinform., 2021

2020
Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification.
BMC Bioinform., 2020

2019
DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.
Comput. Biol. Chem., 2019


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