Computational Biology and Machine Learning
The Kuang lab is interested in developing general machine learning approaches for integrative analysis of large-scale genomic data to understand the molecular characteristics of biological functions and phenotypes. They design theoretically principled methods in the categories of kernel methods, graph-based learning algorithms, sequence alignment methods and various statistical models for a unified analysis of the biological data in a data-driven perspective. Current projects center around the following topics:
- Cancer genomics: Development of graph-based learning algorithms, sequence alignment algorithms and association rule-mining algorithms for building predictive models and mining biomarkers of cancer phenotypes from microarray gene expressions, ArrayCGH DNA copy number variations, SNPs and protein-protein interactions.
- Phenome-genome association analysis: Development of graph-based learning algorithms for analyzing disease and gene associations in a network context.
- Protein remote homology detection: Development of kernel algorithms and label propagation algorithms to infer the correlation between protein-protein interactions, protein structures and functions.
- Semi-supervised learning algorithms: Graph-based learning, transfer learning, sparse group learning and kernel learning methods.
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