A link prediction based unsupervised rank aggregation algorithm for informative gene selection

Abstract

Informative Gene Selection is the process of identifying relevant genes that are significantly and differentially expressed in biological procedures. The microarray experiments conducted for this purpose usually implement only less than a hundred of samples to rank the relevance of over thousands of genes. Many irrelevant genes thus may gain statistical importance due to the randomness caused by the small sample problem, while relevant genes may lose focus in the same way. Overcoming such a problem goes beyond what a single microarray dataset can offer and stresses the use of multiple experiment results, which is defined as rank aggregation. In this paper, we propose a novel link prediction based rank aggregation algorithm for the purpose of informative gene selection. Each rank is transferred into a fully connected and weighted network, in which the nodes represent genes and the weights of links stand for priorities between connected nodes (genes). The integration of multiple gene ranks is then formulated as an optimization problem of link prediction on multiple networks, with criterion function favoring the maximization of weighted consensus among each network. We solve the problem through iterative estimation of weights and maximization of consensus among them. In the experimental evaluation, we demonstrate our method on the Prostate Cancer Dataset and compare it with other baseline methods. The results show that our link prediction based rank aggregation method remarkably outperforms all the compared methods, which proves the effectiveness of our framework in finding informative genes from multiple microarray experimental results.