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

Click to filter author papers

Bioinformatics· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Junsu KoORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

BioinformaticsBMC Medical GenomicsPeer-reviewedJul 22, 2026

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

Soobon Ko, Honglan Li, Hongeun Kim…·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.

3 citations

proteomicsstatistical model

𝕏 PostBluesky

BioinformaticsComputational and Structural Biotechnology JournalPeer-reviewedMar 1, 2026

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

Adam Voshall, Jeongjun Chae, Honglan Li…·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.

DOI 10.34133/csbj.0201

machine learning (classical)protein structure prediction

𝕏 PostBluesky