National Taiwan University CSIE
Computational Systems Biology
Research at the intersection of machine learning, network science, and large-scale biological data.
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Single-cell & Spatial Omics
Computational methods for high-dimensional biological data, from cell-state trajectories and gene regulatory dynamics to spatially resolved and mass spectrometry-based omics.
AI Virtual Cell
Learning predictive models of cellular state that simulate how cells respond to drug and genetic perturbations, turning single-cell atlases into in silico experiments.
Computational Drug Discovery
Predicting drug responses and repurposing candidates by coupling single-cell transcriptomics with molecular structure, docking, and network-based learning.
Cancer Systems Biology
Multi-omics integration and network analysis to dissect tumor heterogeneity, resistance, and therapeutic vulnerabilities in cancer and other human diseases.
Quantum Intelligence for Biological Discovery Emerging
Quantum machine learning for modern omics, as fault-tolerant quantum computing comes within reach. Recent theory demonstrates exponential advantages in classification and dimensionality reduction, including single-cell RNA analysis and compound screening for drug discovery.