M.S. candidate in Medical Systems Biology at Soochow University.

My research focuses on interpretable machine learning, missense mutation interpretation, structural bioinformatics, molecular dynamics, network medicine, and cancer omics.

• Interpretable machine learning for missense mutation interpretation
• Structural bioinformatics and molecular dynamics of disease-associated mutations
• Network medicine and AI-driven drug repositioning
• Single-cell transcriptomics in cancer biology

• PTEN cancer/ASD mutational mechanisms Contributior: Miao Yang, Jinran Wang
• protPheMut interpretable mutation analysis Contributior: Jinran Wang, Miao Yang
• Pathway allosteric mutations and molecular dynamics Contributior: Miao Yang
• LUAD network drug repositioning Contributior: Miao Yang
• Ovarian cancer single-cell analysis Contributior: Miao Yang

• Yang M#, Wang J#, Zhou Z, Li W, Verkhivker G, Xiao F, Hu G. Machine Learning and Structural Dynamics-Based Approach to Reveal Molecular Mechanism of PTEN Missense Mutations Shared by Cancer and Autism Spectrum Disorder. Journal of Chemical Information and Modeling, 2025, 65(8): 4173-4188.

• Wang J#, Yang M#, Zong C, Li Y, Verkhivker G, Xiao F, Hu G. protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations. Journal of Chemical Information and Modeling, 2025, 65(15): 8375-8384.

• .A Subtype-Specific Network Module Identification and Drug Repositioning System Software V1.0. Software Copyright, Registration No. 2025SR142352, 2025. Contributior: Miao Yang
• .ProPheMut Contributior: Jinran Wang, Miao Yang

• Programming: Python, R, Bash/Shell scripting, SQL; experienced in large-scale biological data processing, statistical analysis, visualization, and reproducible computational workflows.
• Machine Learning: scikit-learn, XGBoost, LightGBM, CatBoost, SHAP, Optuna; feature engineering, feature selection, cross-validation, ensemble/stacking models, model interpretation, ROC/PR/calibration analysis.
• Bioinformatics: Mutation data curation, sequence conservation and coevolution analysis, GO/KEGG enrichment, ssGSEA/module scoring, consensus clustering, survival analysis, tumor mutation analysis, single-cell transcriptomic analysis; familiar with limma, sva/ComBat, ConsensusClusterPlus, clusterProfiler, GSVA, maftools, Seurat, and AUCell.
• Structural Bioinformatics & Molecular Dynamics: AlphaFold, PyMOL, GROMACS, MDAnalysis, MD-TASK, FoldX, fpocket; RMSD/RMSF, DCCM, dynamic residue networks, shortest-pathway/allosteric communication analysis, protein pocket analysis, and mutation mechanism interpretation.
• Network Biology: STRING and physical PPI networks, residue interaction networks, network topology analysis, Louvain/Walktrap community detection, disease module identification, and network-based drug repositioning.