CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.03 - May-June
Issue No.03 - May-June (2012 vol.9)
A. Sharma , Lab. of DNA Inf. Anal., Univ. of Tokyo, Tokyo, Japan
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.151
Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r <; h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the relevance of the selected genes in terms of their biological functions.
set theory, bioinformatics, data analysis, feature extraction, genetics, lab-on-a-chip, biological functions, top-r feature selection algorithm, microarray gene expression data, classification accuracy, gene expression data analysis, gene subset, informative subset, Accuracy, Gene expression, Bioinformatics, Classification algorithms, Cancer, Algorithm design and analysis, DNA microarray gene expression data., Feature selection, classification accuracy, top-r features
A. Sharma, "A Top-r Feature Selection Algorithm for Microarray Gene Expression Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 3, pp. 754-764, May-June 2012, doi:10.1109/TCBB.2011.151