Data Mining Techniques in Bioinformatics Analysis
Keywords:
Data Mining, Bioinformatics, Microarray Datasets, k-meansAbstract
Microarray experiments yield vast datasets containing expression data for thousands of genes across a limited number of samples, usually no more than a few dozen. A major challenge is identifying groups of genes that are co-regulated and collectively show strong associations with specific outcome variables. To tackle this challenge, we suggest using k-means clustering algorithms, which leverage external information about response variables to group genes effectively. We propose an algorithm based on logistic regression analysis that integrates gene selection, supervision, gene clustering, and sample classification into a single streamlined process. Through empirical studies on diverse microarray datasets, we demonstrate its ability to pinpoint gene clusters whose expression centroids exhibit robust predictive potential, surpassing conventional methods focused on individual gene analysis. This approach not only promises advancements in medical diagnostics and prognostics but also enhances functional genomics by offering insights into gene function and regulation.
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