geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification

Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification

Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification. In the first stage, Mutual Information based gene selection is applied which selects only the genes that have high information related to the cancer. The genes which have high mutual information value are given as input to the second stage. The Genetic Algorithm based gene selection is applied in the second stage to identify and select the optimal set of genes required for accurate classification. For classification, Support Vector Machine (SVM) is used. The proposed MI-GA gene selection approach is applied to Colon, Lung and Ovarian cancer datasets and the results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.