GoFuzzKegg: Mapping Genes to KEGG Pathways Using an Ontological Fuzzy Rule System
In this paper we present a method for finding the main pathways represented in a set of genes (say obtained from a microarray experiment). The method is based on a fuzzy mapping between genes represented as sets of gene ontology terms and KEGG pathways using a new type of fuzzy rule system called ontological fuzzy rule system (OFRS). As opposed to a crisp mapping, the fuzzy mapping produces a nonzero value even if the gene name is not explicitly listed in a given KEGG pathway. An OFRS is a fuzzy rule system in which the rule memberships are obtained using similarity measures between objects computed based on the gene ontology (GO) annotations. To test our approach, we randomly selected without replacement 10 sets of Arabidopsis thaliana genes from KEGG (each set had 15 genes from 3 different pathways) and tried to predict the pathways they were selected from. Our method was able to find, 90% of the right pathways with a 65% false alarm rate at a p-value of 0.01. The high false alarm rate is due in part to the experimental setting. In a pilot dataset of 526 Arabidopsis thaliana genes we identified 8 clusters which proved to be linked to important pathways such as ATP synthesis and transcription factor