Strategy of finding optimal number of features on gene expression data

Sharma, Alokanand and Koh, C.H. and Imoto, S. and Miyano, S. (2011) Strategy of finding optimal number of features on gene expression data. Electronics Letters, IEE, 47 (8). pp. 480-482. ISSN 0013-5194

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Abstract

Feature selection is considered to be an important step in the analysis of transcriptomes or gene expression data. Carrying out feature selection reduces the curse of the dimensionality problem and improves the interpretability of the problem. Numerous feature selection methods have been proposed in the literature and these methods rank the genes in order of their relative importance. However, most of these methods determine the number of genes to be used in an arbitrarily or heuristic fashion. Proposed is a theoretical way to determine the optimal number of genes to be selected for a given task. This proposed strategy has been applied on a number of gene expression datasets and promising results have been obtained.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Ms Shalni Sanjana
Date Deposited:	25 Jun 2011 03:39
Last Modified:	18 Jul 2012 02:00
URI:	https://repository.usp.ac.fj/id/eprint/4813

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