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Ka Yee Yeung: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Richard M. Karp, Roland Stoughton, Ka Yee Yeung
    Algorithms for choosing differential gene expression experiments. [Citation Graph (0, 0)][DBLP]
    RECOMB, 1999, pp:208-217 [Conf]
  2. Qunhua Li, Chris Fraley, Roger Eugene Bumgarner, Ka Yee Yeung, Adrian E. Raftery
    Donuts, scratches and blanks: robust model-based segmentation of microarray images. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:12, pp:2875-2882 [Journal]
  3. X. Liu, Siva Sivaganesan, Ka Yee Yeung, Junhai Guo, Roger Eugene Bumgarner, Mario Medvedovic
    Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:14, pp:1737-1744 [Journal]
  4. Mario Medvedovic, Ka Yee Yeung, Roger Eugene Bumgarner
    Bayesian mixture model based clustering of replicated microarray data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:8, pp:1222-1232 [Journal]
  5. Ka Yee Yeung, Roger Eugene Bumgarner, Adrian E. Raftery
    Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:10, pp:2394-2402 [Journal]
  6. Ka Yee Yeung, Chris Fraley, A. Murua, Adrian E. Raftery, Walter L. Ruzzo
    Model-based clustering and data transformations for gene expression data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2001, v:17, n:10, pp:977-987 [Journal]
  7. Ka Yee Yeung, David R. Haynor, Walter L. Ruzzo
    Validating clustering for gene expression data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2001, v:17, n:4, pp:309-318 [Journal]
  8. Ka Yee Yeung, Walter L. Ruzzo
    Principal component analysis for clustering gene expression data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2001, v:17, n:9, pp:763-774 [Journal]

  9. Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data. [Citation Graph (, )][DBLP]


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