NDLI: Algorithm for gene selection from DNA-microarray data for disease classification

Content Provider	IEEE Xplore Digital Library
Author	Ikumi, Y. Chakraborty, G.
Copyright Year	2010
Description	Author affiliation: Department of Software and Information Science, Iwate Prefectureal University, Takizawamura, Iwate, Japan 020-0193 (Ikumi, Y.; Chakraborty, G.)
Abstract	In recent years, microarray technology has advanced to such a sophistication that, it is possible to obtain gene-expression level of several thousand genes in a single experiment. Simultaneous measurements of tens of thousands of mRNAs can be performed, in which gene expressions of two samples are compared. Depending on the source of the two compared samples, important investigations, like disease progress, diagnosis, drug response, etc., can be done by analyzing DNA microarray data. When one sample source is a healthy cell, and the other a cancerous one, it is possible to identify changes in particular gene expression with the progress of the disease. The aim of this work is to identify a few number of genes, which as a set of features, could clearly classify the target disease. The problem is defined as an optimization problem, where the target is to find minimum number of genes whose expression data could classify the disease type with minimum classification error. As we view the genes as features, the whole microarray data is of enormously high dimensional, where expression value of most of the genes are irrelevant to the targeted investigation. Moreover, the number of samples are in tens to a maximum of around hundred. Under such situation, identifying and eliminating irrelevant genes is of utmost importance. In this paper, we present a two stage reduction. In Stage 1, the number of genes are reduced from thousands to around hundred. We propose a new algorithm for Stage 1 reduction phase. In Stage 2, the number of selected genes are only a few. We proposed a way to achieve that optimization, without actual experiments.
Starting Page	460
Ending Page	465
File Size	664866
Page Count	6
File Format	PDF
ISBN	9781424468898
e-ISBN	9781424468904
DOI	10.1109/TENCON.2010.5686700
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-21
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Training Diseases DNA Cancer Image color analysis Clustering algorithms
Content Type	Text
Resource Type	Article

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