Identification of biomarker genes for resistance to a pathogen by a novel method for meta-analysis of single-channel microarray datasets

The search for fast and reliable methods allowing for extraction of biomarker genes, e.g. responsible for a plant resistance to a certain pathogen, is one of the most important and highly exploited data mining problem in bioinformatics. Here we describe a simple and efficient method suitable for combining results from multiple single-channel microarray experiments for meta-analysis. A new technique presented here makes use of the fuzzy set logic for the initial gene selection and of the machine learning algorithm AdaBoost to retrieve a set of genes where expression profiles are the most different between the resistant and susceptible classes. As a proof of concept, our method has been applied to the analysis of a gene expression dataset composed of many independent microarray experiments on wheat head tissue, to identify genes that are biomarkers of resistance to the fungus Fusarium graminearum. We used microarray data from many experiments performed on wheat lines of various resistance level. The resulting set of genes was validated by qPCR experiments.