A stable gene selection in microarray data analysis.

BMC Bioinformatics
Kun YangGuohui Lin

Abstract

Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classific...Continue Reading

References

Dec 1, 1987·Journal of Neurochemistry·V Natarajan, H H Schmid
Nov 3, 2001·Genome Research·M XiongJ Zhao
Nov 15, 2001·Proceedings of the National Academy of Sciences of the United States of America·A BhattacharjeeM Meyerson
Dec 4, 2001·Nature Genetics·Scott A ArmstrongStanley J Korsmeyer
Jun 28, 2002·Cancer Cell·Dinesh SinghWilliam R Sellers
Nov 9, 2002·Bioinformatics·Olga G TroyanskayaRuss B Altman
Aug 19, 2005·Journal of Bioinformatics and Computational Biology·Sach Mukherjee, Stephen J Roberts

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Citations

Feb 4, 2009·BMC Bioinformatics·Pei-Chun ChenChuhsing K Hsiao
Oct 22, 2010·BMC Bioinformatics·Rok Blagus, Lara Lusa
Nov 15, 2012·BMC Bioinformatics·Hongyan ZhangZheming Yuan
Oct 4, 2012·Biology Direct·Sebastian Student, Krzysztof Fujarewicz
May 15, 2008·Bioinformation·Wenlong XuHuanqing Feng
Aug 25, 2009·Journal of Biomedical Informatics·Huawen LiuHuijie Zhang
Sep 22, 2015·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Jun Chin AngHaza Nuzly Abdull Hamed
Oct 5, 2007·BMC Bioinformatics·Ji-Gang Zhang, Hong-Wen Deng
Jan 22, 2020·Scientific Reports·Pratik DuttaAviral Kumar
Mar 27, 2020·Medical & Biological Engineering & Computing·Armaghan Davoudi, Hamid Mahmoodian
Aug 29, 2020·BMC Medical Genomics·Yi ShiZe-Guang Han
Sep 16, 2020·Computers in Biology and Medicine·Pratik DuttaSriparna Saha
Nov 18, 2019·PeerJ. Computer Science·Davide NardoneAntonino Staiano

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