Machine Learning Approaches to Gene Duplication and
Transcription Regulation Candidate: Huang-Wen Chen Advisor:
Dennis Shasha   Gene duplication can lead to genetic redundancy
or functional divergence, when duplicated genes evolve
independently or partition the original function. In this
dissertation, we employed machine learning approaches to study
two different views of this problem: 1) Redundome, which
explored the redundancy of gene pairs in the genome of
Arabidopsis thaliana, and 2) ContactBind, which focused on
functional divergence of transcription factors by mutating
contact residues to change binding affinity.  In the Redundome
project, we used machine learning techniques to classify gene
family members into redundant and non-redundant gene pairs in
Arabidopsis thaliana, where sufficient genetic and genomic data
is available. We showed that Support Vector Machines were two-
fold more precise than single attribute classifiers, and
performed among the best within other machine learning
algorithms. Machi