Towards evaluation of inferred gene network

Abstract

Gene network is a representation for gene interactions. A gene collaborates with other genes in order to function. Past researches have successfully inferred gene network from gene expression microarray data. Gene expression microarray data represent different levels of gene expressions for organisms during biological activity such as cell cycle. A framework for gene network inference is to normalize gene expression data, discretize data, learn gene network and evaluate gene interactions. This framework was used to learn the gene network for two S. cerevisiae gene expression datasets (Spellman Cell cycle and Gasch Yeast Stress). Gene interaction inference was also done on data contained in 8 major clusters found by Spellman. The inferred networks were compared to gene interaction data curated by Biogrid. Results from the comparison shows that some of the inferred gene interactions agree with data contained in Biogrid and by referring to curated genetic interactions in Biogrid, we can understand the significance of computationally inferred gene interactions.