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Effects of gene selection and data sampling on prediction of breast cancer treatments

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Date Issued:
2014
Summary:
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.
Title: Effects of gene selection and data sampling on prediction of breast cancer treatments.
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Name(s): 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
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2014
Date Issued: 2014
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 76 p.
Language(s): English
Summary: 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.
Identifier: FA00004292 (IID)
Degree granted: Thesis (M.S.)--Florida Atlantic University, 2014.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Antineoplastic agents -- Development
Breast -- Cancer -- Treatment
Cancer -- Genetic aspects
DNA mircroarrays
Estimation theory
Gene expression
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Links: http://purl.flvc.org/fau/fd/FA00004292
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00004292
Use and Reproduction: 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.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.