Gene selection for tumor classification using a novel bio-inspired multi-objective approach

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.     

An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories

Microarrays have thousands to tens-of-thousands of gene features, but only a few hundred patient samples are available. The fundamental problem in microarray data analysis is identifying genes whose disruption causes congenital or acquired disease in humans. In this paper, we propose a new evolutionary method that can efficiently select a subset of potentially informative genes for support vector machine (SVM) classifiers. The proposed evolutionary method uses SVM with a given subset of gene features to evaluate the fitness function, and new subsets of features are selected based on the estimates of generalization error of SVMs and frequency of occurrence of the features in the evolutionary approach. Thus, in theory, selected genes reflect to some extent the generalization performance of SVM classifiers. We compare our proposed method with several existing methods and find that the proposed method can obtain better classification accuracy with a smaller number of selected genes than the existing methods.     

Approach of the functional evolution of duplicated genes in Saccharomyces cerevisiae using a new classification method based on protein-protein interaction data

The concept of protein function is widely used and manipulated by biologists. However, the means of the concept and its understanding may vary depending on the level of functionality one considers (molecular, cellular, physiological, etc.). Genomic studies and new high-throughput methods of the post-genomic era provide the opportunity to shed a new light on the concept of protein function: protein-protein interactions can now be considered as pieces of incomplete but still gigantic networks and the analysis of these networks will permit the emergence of a more integrated view of protein function. In this context, we propose a new functional classification method, which, unlike usual methods based on sequence homology, allows the definition of functional classes of protein based on the identity of their interacting partners. An example of such classification will be shown and discussed for a subset of Saccharomyces cerevisiae proteins, accounting for 7% of the yeast proteome. The genome of the budding yeast contains 50% of protein-coding genes that are paralogs, including 457 pairs of duplicated genes coming probably from an ancient whole genome duplication. We will comment on the functional classification of the duplicated genes when using our method and discuss the contribution of these results to the understanding of function evolution for the duplicated genes.