Title page for ETD etd-05272005-145543


Type of Document Master's Thesis
Author Grothaus, Gregory
URN etd-05272005-145543
Title Biologically-Interpretable Disease Classification Based on Gene Expression Data
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Murali, T. M. Committee Chair
Choi, Vicky Committee Member
Onufriev, Alexey V. Committee Member
Keywords
  • Classification
  • Biclustering
  • Gene Expression
  • Microarrays
Date of Defense 2005-05-13
Availability unrestricted
Abstract
Classification of tissues and diseases based on gene expression data is a powerful application of DNA microarrays. Many popular classifiers like support vector machines, nearest-neighbour methods, and boosting have been applied successfully to this problem. However, it is difficult to determine from these classifiers which genes are responsible for the distinctions between the diseases. We propose a novel framework for classification of gene expression data based on notion of condition-specific clusters of co-expressed genes called xMotifs. Our xMotif-based classifier is biologically interpretable: we show how we can detect relationships between xMotifs and gene functional annotations. Our classifier achieves high-accuracy on leave-one-out cross-validation on both two-class and multi-class data. Our technique has the potential to be the method of choice for researchers interested in disease and tissue classification.
Files
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