An Artificial Fish Swarm based supervised gene rank aggregation algorithm for informative genes studies

As the widespread use of high-throughput genomic and protein analysis, more and more experiments have been done to identify the informative genes for various diseases, thus it provides researchers an opportunity to aggregate across multiple microarray experiments via a rank aggregation approach. However, most of current's microarray rank aggregation methods are either unweighted or prespecified weighted, which has obvious defects. In this paper, We define a new method to weight each ranked list automatically by considering its distances to the other ranked lists and the agreement with some priori knowledge. Then the problem of integrating ranked lists can be formulated as minimizing an objective criterion which is proved to be NP-hard. Accordingly, we use an Artificial Fish Swarm algorithm (AFSA) to solve the well-defined minimization problem of rank aggregation in terms of decision theory. We conduct two sets of experiments to evaluate the performance of our methods. The experimental results show that the proposed approach owns not only the capability of solving optimization problem but also the biological meaning.