AIBioMed Lab
The Artificial Intelligence for Biomedicine (AIBioMed) Lab, led by Dr. Le Nguyen Quoc Khanh at Taipei Medical University (TMU), Taiwan, develops innovative artificial intelligence and computational approaches to address challenging problems in biomedical research and healthcare.
Our research integrates machine learning, deep learning, foundation models, bioinformatics, and computational biology, with applications spanning genomics, multi-omics, medical imaging, molecular interactions, and precision medicine.
We aim to develop reliable, interpretable, and generalizable AI methods that advance biological understanding and support biomedical discovery and clinical research.
Research Areas
Our research focuses on four interconnected directions.
| Biomedical Foundation Models Foundation models and multimodal AI for biological sequences, molecular interactions, and drug discovery. | Genomics and Multi-omics AI for cancer genomics, single-cell and spatial transcriptomics, and multi-omics integration. |
| AI for Medical Imaging Deep learning and multimodal AI for medical image analysis, diagnosis, and prognosis. | Trustworthy Biomedical AI Interpretable, robust, and reproducible AI through rigorous evaluation and external validation. |
Selected Research Highlights
Our recent research includes methodological developments and biomedical applications published in leading international journals.
GRASSP: RNA Language Model–Enhanced Graph Attention with Adaptive Gating for RNA–Small Molecule Binding Site Prediction
Bioinformatics, 2026.
A graph-based learning framework integrating RNA language-model representations for RNA–small molecule binding-site prediction.3DICE: Interpretable 3D Cross-Modal Learning for Drug–Target Interaction Prediction and Large-Scale Drug Discovery
Bioinformatics, 2026.
An interpretable cross-modal learning approach for drug–target interaction prediction.Robust Prediction of Drug Interactions using Chemical Descriptors
npj Digital Medicine, 2026.
A computational approach to predicting drug interactions using chemical descriptors.Bayesian Hyperparameter Optimization Improves scGPT Fine-Tuning for Single-Cell Multi-Omics Integration
Bioinformatics, 2026.
An investigation of Bayesian optimization for improving foundation-model adaptation in single-cell multi-omics analysis.RIMGOGraph: Integrating AlphaFold-Derived Residue Interaction Graphs and Protein Language Embeddings for Structure-Informed Protein Function Prediction
International Journal of Biological Macromolecules, 2026.
A structure-informed framework combining residue interaction graphs and protein language-model embeddings for protein function prediction.
Latest News
August 22, 2026 — Our paper GRASSP was published in Bioinformatics.
July 20, 2026 — Congratulations to Bui Xuan Lam on successfully defending her Master’s thesis and earning her M.S. degree.
July 20, 2026 — Our paper Robust Prediction of Drug Interactions using Chemical Descriptors was published in npj Digital Medicine.
July 17, 2026 — Congratulations to Nguyen Thanh Huy on successfully defending his doctoral dissertation and earning his Ph.D. degree.
July 15, 2026 — Congratulations to Nguyen Ky Phat on successfully defending his Master’s thesis and earning his M.S. degree.
July 2, 2026 — Our paper 3DICE was published in Bioinformatics.
June 12, 2026 — Our paper on Bayesian optimization for scGPT fine-tuning was published in Bioinformatics.
Our Team
AIBioMed brings together graduate students, medical professionals, and research interns from diverse academic backgrounds, including computer science, bioinformatics, biomedical sciences, and medicine.
Our group promotes interdisciplinary collaboration, methodological innovation, and rigorous scientific research. We provide research training and mentorship through Master’s and Ph.D. programs, as well as short-term research internships.
Join AIBioMed
We welcome motivated students interested in artificial intelligence, bioinformatics, computational biology, and AI-driven biomedical research.
Opportunities may be available for:
- Ph.D. students interested in developing innovative computational and AI methodologies for biomedical applications.
- Master’s students seeking research training in biomedical AI and bioinformatics.
- Research interns interested in short-term research projects, typically lasting 3–6 months.
Applicants from computer science, engineering, medicine, biomedical sciences, and related disciplines are encouraged to explore opportunities with our group.
Prospective applicants are welcome to contact the Principal Investigator with a CV, a brief statement of research interests, and their expected starting date.
Meet Our Team | Explore Our Research
About the Principal Investigator
Dr. Le Nguyen Quoc Khanh is an Associate Professor in the College of Medicine at Taipei Medical University, Taiwan, and leads the AIBioMed Lab.
He is affiliated with the In-Service Master Program in Artificial Intelligence in Medicine, the International Master/Ph.D. Program in Medicine, the International Ph.D. Program for Cell Therapy and Regeneration Medicine, and the TMU Research Center for Artificial Intelligence in Medicine.
Before joining TMU, he was a Research Fellow at Nanyang Technological University, Singapore. He earned his Master’s and Ph.D. degrees from Yuan Ze University, Taiwan, specializing in computer science and biomedical informatics.
His research focuses on artificial intelligence, bioinformatics, computational biology, and biomedical applications of machine learning.