Graduation Year
2006
Document Type
Thesis
Degree
M.S.Cp.E.
Degree Granting Department
Computer Science and Engineering
Major Professor
Dmitry B. Goldgof, Ph.D.
Co-Major Professor
Lihua Li, Ph.D.
Committee Member
Lawrence O. Hall, Ph.D.
Keywords
Parametric Methods, Nonparametric Methods, Classification and Prediction Simulated Experiment, Biological Application
Abstract
DNA microarrays have been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. One of the major goals of microarray data analysis is the detection of differentially expressed genes across two kinds of tissue samples or samples obtained under two experimental conditions. A large number of gene detection methods have been developed and most of them are based on statistical analysis. However the statistical analysis methods have the limitations due to the small sample size and unknown distribution and error structure of microarray data. In this thesis, a study of ranking-based gene selection methods which have weak assumption about the data was done. Three approaches are proposed to integrate the individual ranks to select differentially expressed genes in microarray data. The experiments are implemented on the simulated and biological microarray data, and the results show that ranking-based methods outperform the t-test and SAM in selecting differentially expressed genes, especially when the sample size is small.
Scholar Commons Citation
Chen, Li, "Ranking-Based Methods for Gene Selection in Microarray Data" (2006). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/3888
