Ka Yee Yeung

Summary

Affiliation: University of Washington
Country: USA

Publications

  1. pmc Integrating external biological knowledge in the construction of regulatory networks from time-series expression data
    Kenneth Lo
    Department of Microbiology, University of Washington, Box 358070, Seattle, WA 98195, USA
    BMC Syst Biol 6:101. 2012
  2. pmc Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data
    K Y Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Bioinformatics 28:823-30. 2012
  3. pmc Clustering gene-expression data with repeated measurements
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 4:R34. 2003
  4. ncbi request reprint Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Bioinformatics 21:2394-402. 2005
  5. pmc From co-expression to co-regulation: how many microarray experiments do we need?
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 5:R48. 2004
  6. pmc Multiclass classification of microarray data with repeated measurements: application to cancer
    Ka Yee Yeung
    Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA
    Genome Biol 4:R83. 2003
  7. pmc Construction of regulatory networks using expression time-series data of a genotyped population
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Proc Natl Acad Sci U S A 108:19436-41. 2011
  8. pmc Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data
    Amalia Annest
    Institute of Technology Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA
    BMC Bioinformatics 10:72. 2009
  9. pmc The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data
    Vivian G Oehler
    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
    Blood 114:3292-8. 2009
  10. pmc MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis
    Vu T Chu
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 9:R118. 2008

Research Grants

Detail Information

Publications13

  1. pmc Integrating external biological knowledge in the construction of regulatory networks from time-series expression data
    Kenneth Lo
    Department of Microbiology, University of Washington, Box 358070, Seattle, WA 98195, USA
    BMC Syst Biol 6:101. 2012
    ..We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge...
  2. pmc Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data
    K Y Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Bioinformatics 28:823-30. 2012
    ....
  3. pmc Clustering gene-expression data with repeated measurements
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 4:R34. 2003
    ..In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results...
  4. ncbi request reprint Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Bioinformatics 21:2394-402. 2005
    ..BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes)...
  5. pmc From co-expression to co-regulation: how many microarray experiments do we need?
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 5:R48. 2004
    ..However, clustering results may not have any biological relevance...
  6. pmc Multiclass classification of microarray data with repeated measurements: application to cancer
    Ka Yee Yeung
    Department of Microbiology, Box 358070, University of Washington, Seattle, WA 98195, USA
    Genome Biol 4:R83. 2003
    ..We show that removing highly correlated genes typically improves classification results using a small set of genes...
  7. pmc Construction of regulatory networks using expression time-series data of a genotyped population
    Ka Yee Yeung
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Proc Natl Acad Sci U S A 108:19436-41. 2011
    ..Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature...
  8. pmc Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data
    Amalia Annest
    Institute of Technology Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA
    BMC Bioinformatics 10:72. 2009
    ..Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes...
  9. pmc The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data
    Vivian G Oehler
    Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
    Blood 114:3292-8. 2009
    ..BMA is a powerful tool for developing diagnostic tests from microarray data. Because therapeutic outcomes are so closely tied to disease phase, these probabilities can be used to determine a risk-based treatment strategy at diagnosis...
  10. pmc MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis
    Vu T Chu
    Department of Microbiology, University of Washington, Seattle, WA 98195, USA
    Genome Biol 9:R118. 2008
    ..We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA...
  11. doi request reprint Methods for the inference of biological pathways and networks
    Roger E Bumgarner
    Department of Microbiology, University of Washington, Seattle, WA, USA
    Methods Mol Biol 541:225-45. 2009
    ..This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes...
  12. ncbi request reprint Donuts, scratches and blanks: robust model-based segmentation of microarray images
    Qunhua Li
    Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA
    Bioinformatics 21:2875-82. 2005
    ..It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three...
  13. ncbi request reprint Bayesian robust inference for differential gene expression in microarrays with multiple samples
    Raphael Gottardo
    Department of Statistics, University of Washington, Box 354322, Seattle, Washington 98195, USA
    Biometrics 62:10-8. 2006
    ..In an experiment with HIV data, our method performed better than these alternatives, on the basis of between-replicate agreement and disagreement...

Research Grants6

  1. Improved Pattern Recognition for Functional Genomics
    KA YEE YEUNG RHEE; Fiscal Year: 2006
    Computational methods have become intrinsic to biomedical research. The overall goal is to provide Dr. Ka Yee Yeung (Ph.D...
  2. Prediction and Network Construction Using High-throughput Data
    KA YEE YEUNG RHEE; Fiscal Year: 2009
    ..This project could lead to inexpensive, accurate and robust diagnostic tests that increase the accuracy of diagnoses or prognoses for patients with cancer or other diseases. ..
  3. Prediction and Network Construction Using High-throughput Data
    KA YEE YEUNG RHEE; Fiscal Year: 2010
    ..This project could lead to inexpensive, accurate and robust diagnostic tests that increase the accuracy of diagnoses or prognoses for patients with cancer or other diseases. ..
  4. Prediction and Network Construction Using High-throughput Data
    KA YEE YEUNG RHEE; Fiscal Year: 2009
    ..This project could lead to inexpensive, accurate and robust diagnostic tests that increase the accuracy of diagnoses or prognoses for patients with cancer or other diseases. ..