Diagnosis of cancer very often depends on conclusions drawn after both clinical and
microscopic examinations of tissues to study the manifestation of the disease in order
to place tumors in known categories. One factor which determines the categorization
of cancer is the tissue from which the tumor originates. Information gathered from
clinical exams may be partial or not completely predictive of a specific category of
cancer. Further complicating the problem of categorizing various tumors is that
the histological classification of the cancer tissue and description of its course of
development may be atypical.
Gene expression data gleaned from micro-array analysis provides tremendous
promise for more accurate cancer diagnosis. One hurdle in the classification of tumors
based on gene expression data is that the data space is ultra-dimensional with relatively
few points; that is, there are a small number of examples with a large number
of genes. A second hurdle is expression bias caused by the correlation of genes.
Analysis of subsets of genes, known as gene set analysis, provides a mechanism by
which groups of differentially expressed genes can be identified. We propose an ensemble
of classifiers whose base classifiers are ℓ1-regularized logistic regression models
with restriction of the feature space to biologically relevant genes. Some researchers
have already explored the use of ensemble classifiers to classify cancer but the effect
of the underlying base classifiers in conjunction with biologically-derived gene sets on
cancer classification has not been explored.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-05272013-105338 |
| Date | 20 June 2013 |
| Creators | Duncan, William Evans |
| Contributors | Wang, Wei-Hsung, Zhang, Jian, Karki, Bijaya, Chen, Jinhua |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-05272013-105338/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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