Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13953
Title: Gene selection and tissue classification with microarray data
Authors: HAO YING
Keywords: Gene, Microarray, Classification, Gene Selection, Support Vector Machines, Fisher's Linear Discriminant
Issue Date: 18-Jun-2004
Source: HAO YING (2004-06-18). Gene selection and tissue classification with microarray data. ScholarBank@NUS Repository.
Abstract: Tumor classification is one of the important applications of microarray technology. In gene expression-base tumor classification systems, gene selection is a main and very important component. In this thesis, firstly we sumarize some important methods of classification, such as Fishera??s Linear Discriminant, Bayesian classification, and Support Vector Machines. Then, we review some useful gene selection approaches including prediction strength method, pre-filter method, gene selection by mathematical programming and a very robust approach called nonparametric scoring method. At last, we propose a new method for gene selection. With the genes selected in colon cancer data, and acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data by using our approach, we apply Fishera??s linear discriminant and Support Vector Machines to classify tissues in these two data sets, respectively. The results of classification show that our method is very useful and promising.
URI: http://scholarbank.nus.edu.sg/handle/10635/13953
Appears in Collections:Master's Theses (Open)

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