Kunjie Fan
Biochemistry, Genetics and Molecular Biology · The Ohio State University
Publications
23
Citations
426
Est. group size
~6
Recurring co-author estimate
Active years
6
Publishing since 2020
Kunjie Fan works at the intersection of computational biology and machine learning, developing tools to analyze genetic and protein interaction data. Their work includes predicting how combinations of genes affect cells (relevant to cancer treatment and CRISPR gene-editing screens), inferring protein functions using network-based deep learning, and applying AI to text-mining tasks like finding drug-drug interaction information in scientific literature. This research supports drug discovery and helps identify potential therapeutic targets, such as for chemo-resistant breast cancer.
Publication output was minimal before 2020, peaked with 7 papers in 2020, and has since settled into a steady pace of roughly 3-5 publications per year through 2024-2025.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo)
PLoS Computational Biology · 2025
- GenRCA: a user-friendly rare codon analysis tool for comprehensive evaluation of codon usage preferences based on coding sequences in genomes
BMC Bioinformatics · 2024
- Multi-Layer Encoder Prediction of Gene Combination Effect (MLE-GeneCombo) Using CRISPR-Cas9 Double Knockout Data
SSRN Electronic Journal · 2024
- Identifying Novel Therapeutic Targets for Overcoming TNBC Chemo Resistance Through Comprehensive CRISPR-Cas9 Genome Screening
bioRxiv (Cold Spring Harbor Laboratory) · 2024
- Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature
Journal of Biomedical Semantics · 2023
- Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature
Research Square · 2022
- Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects
Briefings in Bioinformatics · 2021
- Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
BMC Bioinformatics · 2020
- Graph2GO: a multi-modal attributed network embedding method for inferring protein functions
GigaScience · 2020
- Pseudo2GO: A Graph-Based Deep Learning Method for Pseudogene Function Prediction by Borrowing Information From Coding Genes
Frontiers in Genetics · 2020
- Frontiers in Genetics×3
- BMC Bioinformatics×2
- Briefings in Bioinformatics×2
- bioRxiv (Cold Spring Harbor Laboratory)×2
- International Journal of Computational Biology and Drug Design×2
- Yang Huo
Biochemistry, Genetics and Molecular Biology · The Ohio State University
- Lijun Cheng
Biochemistry, Genetics and Molecular Biology · The Ohio State University
- Min Wu
Biochemistry, Genetics and Molecular Biology · The Ohio State University
- Hojin Yoo
Biochemistry, Genetics and Molecular Biology · The Ohio State University
- Yijie Wang
Biochemistry, Genetics and Molecular Biology · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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