In recent years more and more researchers have begun to use data mining and
machine learning tools to analyze gene microarray data. In this thesis we have collected a
selection of datasets revolving around prediction of patient response in the specific area
of breast cancer treatment. The datasets collected in this paper are all obtained from gene
chips, which have become the industry standard in measurement of gene expression. In
this thesis we will discuss the methods and procedures used in the studies to analyze the
datasets and their effects on treatment prediction with a particular interest in the selection
of genes for predicting patient response. We will also analyze the datasets on our own in
a uniform manner to determine the validity of these datasets in terms of learning potential
and provide strategies for future work which explore how to best identify gene signatures. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_30781 |
| Contributors | Heredia, Brian (author), Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 76 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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