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Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach

Publication ,  Conference
Bi, C; Becker, M; Leeder, S
Published in: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
September 28, 2011

NCBI has been accumulating a large repository of microarray data sets, namely Gene Expression Omnibus (GEO). GEO is a great resource enabling one to pursue various biological and pathological questions. The question we ask here is: given a set of gene signatures and a classifier, what is the best minimum sample size in a clinical microarray research that can effectively distinguish different types of patient responses to a therapeutic drug. It is difficult to answer the question since the sample size for most microarray experiments stored in GEO is very limited. This paper presents a Monte Carlo approach to simulating the best minimum microarray sample size based on the available data sets. Support Vector Machine (SVM) is used as a classifier to compute prediction accuracy for different sample size. Then, a logistic function is applied to fit the relationship between sample size and accuracy whereby a theoretic minimum sample size can be derived. © 2011 IEEE.

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IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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Publication Date

September 28, 2011

Start / End Page

129 / 134
 

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Bi, C., Becker, M., & Leeder, S. (2011). Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach. In IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (pp. 129–134). https://doi.org/10.1109/CIBCB.2011.5948461
Bi, C., M. Becker, and S. Leeder. “Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach.” In IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 129–34, 2011. https://doi.org/10.1109/CIBCB.2011.5948461.
Bi C, Becker M, Leeder S. Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach. In: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 2011. p. 129–34.
Bi, C., et al. “Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach.” IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2011, pp. 129–34. Scopus, doi:10.1109/CIBCB.2011.5948461.
Bi C, Becker M, Leeder S. Derivation of minimum best sample size from microarray data sets: A Monte Carlo approach. IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 2011. p. 129–134.

Published In

IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIBCB 2011: 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

DOI

Publication Date

September 28, 2011

Start / End Page

129 / 134