Author

Chonghao Wang

Hong Kong Baptist UniversityORCID 0000-0002-4259-464XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:GWAS summary statistics· 3 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Assay:ATAC-seq· 1

Frequent Co-Authors

Lu ZhangiD· 6 shared Yu Gong XuiD· 3 shared Aiping LüiD· 2 shared Werner Pieter VeldsmaniD· 2 shared Zeming LiiD· 2 shared Jing ZhangiD· 1 shared Ke XuiD· 1 shared Louxin Zhang· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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6 shared papersBioinformatics

Lu ZhangORCID

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6 papers

Recent·Most cited

Illuminating phage dark matter beyond the reach of structural alignments with PhageFlare

Chonghao Wang, Yu Gong Xu, Zeming Li, Louxin Zhang, Lu Zhang

PhageFlare predicts functions for phage proteins with no structural matches, outperforming baselines and annotating many previously unresolved proteins.

bioRxiv · Oct 29, 2026 · PreprintMicrobiology

Enhanced disease susceptible variant identification via short identity by descent segments

Chonghao Wang, Werner Pieter Veldsman, Yufen Huang, Xiaodong Fang, Lu Zhang

SILO improves detection of disease susceptible variants in short IBD segments, outperforming existing models in simulated and real data.

IEEE Transactions on Computational Biology and Bioinformatics · Mar 31, 2026Peer-reviewedBioinformatics

TRAFICA: An Open Chromatin Language Model to ImproveTranscription Factor Binding Affinity Prediction

Yu Gong Xu, Chonghao Wang, Ke Xu, Yi Ding, Aiping Lü, Lu Zhang

TRAFICA improves TF-DNA binding affinity prediction by integrating open chromatin features and in vitro binding profiles, outperforming existing tools in both in vitro and in vivo settings.

Bioinformatics · Aug 22, 2025Peer-reviewedBioinformatics

A structure-informed evolutionary model for predicting viral immune escape and evolution

Chonghao Wang, Lu Zhang

This paper introduces KEScape, a deep learning model that predicts viral immune escape and evolution by integrating structural information and prioritizing impactful mutations, outperforming existing models.

bioRxiv · Jul 31, 2025 · PreprintMicrobiology

A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

Yu Gong Xu, Chonghao Wang, Zeming Li, Yunpeng Cai, Ouzhou Young, Aiping Lü, Lu Zhang

This paper introduces PRSIMD, a machine learning model that integrates genetic and non-genetic factors to improve disease risk prediction, showing better performance than existing PRS methods.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · Dec 6, 2022Peer-reviewedBioinformatics

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants

Chonghao Wang, Jing Zhang, Werner Pieter Veldsman, Xin Zhou, Lu Zhang

This study compares statistical and machine learning methods for predicting complex diseases using genomic variants, finding statistical methods generally outperform machine learning and ensemble models like Super Learner perform best.

Briefings in Bioinformatics · Nov 15, 2022Peer-reviewedBioinformatics