Establishing an association between variables is always of interest in genomic studies. Generation of DNA microarray gene expression data introduces a variety of data analysis issues not encountered in traditional molecular biology or medicine. Frequent pattern mining (FPM) has been applied successfully in business and scientific data for discovering interesting association patterns, and is becoming a promising strategy in microarray gene expression analysis. We review the most relevant FPM strategies, as well as surrounding main issues when devising efficient and practical methods for gene association analysis (GAA). We observed that, so far, scalability achieved by efficient methods does not imply biological soundness of the discovered association patterns, and vice versa. Ideally, GAA should employ a balanced mining model taking into account best practices employed by methods reviewed in this survey. Integrative approaches, in which biological knowledge plays an important role within the mining process, are becoming more reliable.

Supplementary data are available at Bioinformatics online.

No abstract

In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.