Graph coloring for extracting discriminative genes in cancer data.

Background And Objective: The major difficulty of the analysis of the input gene expression data in a microarray-based approach for an automated diagnosis of cancer is the large number of genes (high dimensionality) with many irrelevant genes (noise) compared to the very small number of samples. This research study tackles the dimensionality reduction challenge in this area.

Methods: This research study introduces a dimension-reduction technique termed graph coloring approach (GCA) for microarray data-based cancer classification based on analyzing the absolute correlation between gene-gene pairs and partitioning genes into several hubs using graph coloring.

Pairwise gene GO-based measures for biclustering of high-dimensional expression data.

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO).

An application of the Shapley value to the analysis of co-expression networks.

We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex biological pathways of interest. An application of the method to the analysis of gene expression data from microarrays is presented, as well as a comparison with classical centrality indices.

Integrating biological knowledge based on functional annotations for biclustering of gene expression data.

Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect to the same experimental condition and not due to the same regulatory regime. For this reason, traditional techniques are recently being improved with the use of prior biological knowledge from open-access repositories together with gene expression data.

Biclustering of gene expression data by correlation-based scatter search.

Background: The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed.