Author

Honglan Li

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Junsu KoiD· 2 shared Yoonjoo ChoiiD· 2 shared Adam VoshalliD· 1 shared Eunjung Alice LeeiD· 1 shared Hongeun Kim· 1 shared Jeongjun ChaeiD· 1 shared Naimur RahmaniD· 1 shared Soobon KoiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Junsu KoORCID

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2 papers

Recent·Most cited

Benchmarking Sequence-Based and AlphaFold-Based Methods for pMHC-II Binding Core Prediction: Distinct Strengths and Consensus Approaches

Soobon Ko, Honglan Li, Hongeun Kim, Woong‐Hee Shin, Junsu Ko, Yoonjoo Choi

This paper benchmarks sequence-based and AlphaFold-based methods for predicting pMHC-II binding cores, finding that AlphaFold-based methods excel at identifying binders while NetMHCIIpan is better at identifying non-binders.

BMC Medical Genomics · Jul 22, 2026Peer-reviewedBioinformatics

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features

Adam Voshall, Jeongjun Chae, Honglan Li, Junsu Ko, Naimur Rahman, Woongyang Park, Eunjung Alice Lee, Yoonjoo Choi

STRUMP-I improves pMHC-I binding prediction by using force-field energy features, outperforming sequence-based methods on underrepresented MHC alleles and showing synergy with them.

Computational and Structural Biotechnology Journal · Mar 1, 2026Peer-reviewedBioinformatics