Author

Fang He

Pharmaron (China)

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

Same name, identified by ORCID

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 3 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Organism:human· 1 Approach:optimal transport· 1 Assay:protein structure· 1

Frequent Co-Authors

Kaiqiang Hu· 3 shared Pengwei PaniD· 3 shared Zhe LiiD· 3 shared Ye YuaniD· 2 shared Li Feng· 1 shared Qiuyuan Yang· 1 shared Yunyang ZhangiD· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

7 top collaborators · 18 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

3 shared papersBioinformatics

Kaiqiang Hu

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning

Kaiqiang Hu, Yunyang Zhang, Li Feng, Qiuyuan Yang, Zhe Li, Pengwei Pan, Fang He

This study uses Random Forest regression to predict cancer cell growth kinetics, showing higher accuracy than logistic and Gompertz models at high seeding densities.

bioRxiv · Sep 25, 2026 · PreprintBioinformatics

Optimized Multiple Circular Sequence Alignment for Cyclic Peptide Motif Discovery

Ye Yuan, Zhe Li, Kaiqiang Hu, Pengwei Pan, Fang He

The paper optimizes multiple circular sequence alignment for cyclic peptides, achieving faster performance while maintaining alignment quality.

bioRxiv · Sep 21, 2026 · PreprintBioinformatics

Benchmarking confidence estimation and rescoring for cyclic peptide-protein complex predictions

Zhe Li, Ye Yuan, Kaiqiang Hu, Pengwei Pan, Fang He

This study benchmarks confidence estimation and rescoring methods for cyclic peptide-protein complex predictions, finding that pose ranking is the main limitation and that structural features improve confidence-based selection.

bioRxiv · Sep 21, 2026 · PreprintBioinformatics