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Research

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The general research objective of our lab is to develop machine learning algorithms to extract and integrate subtle and elusive information hiding in genome-wide large-scale biological data for understanding the association between genomic characteristics and phenotypes. We are particularly interested in designing theoretically principled methods in the categories of kernel methods, graph-based learning algorithms and various other statistical models for a unified analysis of the high-throughput data in a data-driven perspective: learn from known data accurate predictive models and discover from known data essential and key elements to characterize and predict phenotypes. Our current projects center around the following topics,

• Cancer genomics: we design graph-based learning algorithms for mining biomarkers of disease-related phenotypes from microarray gene expressions, SNPs and proteomic data.
• Identifying malaria parasite proteins for drug design: we apply remote homology detection algorithms to detecting drug targets from unidentified proteins in malaria parasite species.
• Analysis of protein functions and structures: we use machine learning algorithms to infer the correlation between protein-protein interactions, protein structures and functions.