Mingyan Fang
Research Area: Genomics, Computational Biology
Principal Investigator specializing at the intersection of Genomics and Artificial Intelligence, with a Ph.D. in Clinical Immunology from Karolinska Institutet, Sweden. Over 16 years of experience in computational biology and translational medicine, dedicated to deciphering the mechanisms of immune diseases through innovative AI/ML approaches and large-scale multi-omics integration.
Research Highlights
AI/Algorithm Innovation
Developed VIPPID (first specialized Primary Immunodeficiency variant predictor), GeneRAIN (Transformer-based GRN model), and VIPER (LLM for genetic disease gene identification).
Disease Mechanism Research
Systematically identified 50+ causative genes for human disease (Inborn error of immunity, autoimmune, neuroimmune, etc.), elucidating pathogenic mechanisms.
Large-Scale Data Platforms
Developed the ZBOLT genomic analysis platform with a capacity of 100 Tbp/day, enabling ultra-large-scale population studies.
Selected Publications
Representative works from recent years. See the full list on the Publications page.
Population-scale genomic screening reveals high frequency of actionable secondary findings in Chinese newborns
Y. Huang, Y. Gao, Z. Duan, X. Jia, Y. Sun, C. Liu, H. Huang, J. Liu, S. Pan, X. Jin, M. Fang#. npj Genomic Medicine, 11(1):29 (2026).
Population-scale genomic screening study identifying high frequency of actionable secondary findings in Chinese newborns, informing newborn genomic screening policy and clinical practice.
Harnessing Artificial Intelligence for Genomic Variant Prediction: Advances, Challenges, and Future Directions
I. Pakpahan*, M. Sihombing*, H. Liu, M. Wang, Z. Su#, M. Fang#. GigaScience, giag004 (2026).
Comprehensive review of advances, challenges, and future directions in harnessing artificial intelligence for genomic variant prediction.
GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks
Z. Su*, M. Fang*, A. Smolnikov, ME. Dinger, EC. Oates, F. Vafaee. Genome Biology 26, 288 (2025).
Novel deep learning framework for gene representation learning using 777K bulk transcriptomes, advancing AI-driven genomics research.
Age-Related Dynamics and Spectral Characteristics of the TCRβ Repertoire in Healthy Children: Implications for Immune Aging
Fang M#*, Y. Miao#, L. Zhu, Y. Mei, H. Zeng, L. Luo, Y. Ding, L. Zhou, X. Quan, Q. Zhao, X. Zhao, Y. An#. Aging Cell, 2025.
Characterizes age-related dynamics in the TCRβ repertoire of healthy children, informing immune aging mechanisms.
An efficient large‐scale whole‐genome sequencing analyses practice with an average daily analysis of 100Tbp
Z. Li*, Y. Xie*, W. Zeng*, Y. Huang, S. Gu, Y. Gao, W. Huang, L. Lu, X. Wang, J. Wu, X. Yin, R. Zhu, G. Huang, L. Lu, J. Tang, Y. Zheng, Q. Liu, X. Zhou, R. Shan#, B. Wang#, M. Fang#, X. Jin#. Clinical and Translational Discovery, 2023.
Development of ZBOLT platform enabling ultra-large-scale genomic data processing at 100 Tbp/day capacity.
VIPPID: a gene specific single nucleotide variant pathogenicity prediction tool for Primary Immunodeficiency Diseases
M. Fang*, Z. Su*, H. Abolhassani, Y. Itan, X. Jin, L. Hammarström. Briefings in Bioinformatics, bbac176 (2022).
First specialized variant pathogenicity predictor for Primary Immunodeficiency Diseases, revolutionizing clinical genetic diagnosis.
T Cell Repertoire Abnormality in Immunodeficiency Patients with DNA Repair and Methylation Defects
M. Fang*, Z. Su*, H. Abolhassani*, W. Zhang, C. Jiang, B. Cheng, L. Luo, J. Wu, S. Wang, L. Lin, X. Wang, L. Wang, A. Aghamohammadi, T. Li, X. Zhang, L. Hammarström, X. Liu. Journal of Clinical Immunology 42, 375-393 (2022).
Characterizes distinct TCR repertoire abnormalities in immunodeficiency patients with DNA repair and methylation defects, linking early thymic T cell development defects to heterogeneous disease phenotypes.