title: Identification of biomarker genes for resistance to a pathogen by a novel method for meta-analysis of single-channel microarray datasets.
abstract: 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.
authors.affiliation: Institute of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland.
authors.forename: Piotr Iwo
authors.initials: PI
authors.lastname: Wójcik
authors.affiliation: Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Ave, K1A 0C6 Ottawa, Canada.
authors.forename: Thérèse
authors.initials: T
authors.lastname: Ouellet
authors.affiliation: Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Ave, K1A 0C6 Ottawa, Canada.
authors.forename: Margaret
authors.initials: M
authors.lastname: Balcerzak
authors.affiliation: Institute of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland.
authors.forename: Witold
authors.initials: W
authors.lastname: Dzwinel
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D060467:Disease Resistance / Q000235:genetics; D005670:Fusarium / Q000472:pathogenicity*; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D005819:Genetic Markers; D000069550:Machine Learning; D015201:Meta-Analysis as Topic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D014908:Triticum / Q000235:genetics* / Q000502:physiology*
chemical_list: D005819:Genetic Markers
keywords: AdaBoost classifier; Gene selection; differentially expressed genes; fusarium head blight; fuzzy set representation; gene ranking; wheat resistance