Our lab is interested in developing general machine learning models and algorithms for integrative analysis of large-scale genomic data to understand the molecular characteristics of biological functions and phenotypes. We design mathematically principled methods in the categories of graph-based semi-supervised learning, transfer learning, string kernels and other kernel methods, sequence alignment methods and various statistical models for a unified analysis of heterogeneous biological data. Our 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 or sequencing transcriptome data, 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 string kernel algorithms and label propagation algorithms to infer the protein remote homologys and study their protein structures and functions.
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    Zhang, Wei; Hwang, Baryun; Wu, Baolin; Kuang, Rui (2010): Network propagation models for gene selection . In: 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), IEEE, 2010, ISBN: 978-1-61284-791-7. (Type: Inproceedings | Abstract | Links)
    Fang, Gang; Kuang, Rui; Pandey, Gaurav; Steinbach, Michael; Myers, Chad; Kumar, Vipin (2010): Subspace differential coexpression analysis: problem definition and a general approach. . In: Pacific symposium on biocomputing, pp. 145–56, 2010. (Type: Inproceedings | Abstract | Links)
    Gupta, Rohit; Agrawal, Smita; Rao, Navneet; Tian, Ze; Kuang, Rui; Kumar, Vipin (2009): Integrative Biomarker Discovery for Breast Cancer Metastasis from Gene Expression and Protein Interaction Data Using Error-tolerant Pattern Mining . In: Citeseer, 2009. (Type: Inproceedings | Abstract | Links)
    Tian, Ze; Hwang, TaeHyun; Kuang, Rui (2009): A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge . In: Bioinformatics, 25 (21), pp. 2831–2838, 2009, ISSN: 1460-2059. (Type: Journal Article | Abstract | Links)
    Hwang, TaeHyun; Tian, Ze; Kuang, Rui; Kocher, Jean-Pierre (2008): Learning on weighted hypergraphs to integrate protein interactions and gene expressions for cancer outcome prediction . In: 2008 Eighth IEEE International Conference on Data Mining, pp. 293–302, IEEE 2008, ISBN: 978-0-7695-3502-9. (Type: Inproceedings | Abstract | Links)
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  • Semi-supervised and transfer learning algorithms: Development of general and scalable graph-based learning, transfer learning, sparse group learning and kernel learning method.