Influence of algorithmic parameters on marker selection in genomic datasets

The biological processes are widely studied by genome analysis leading to a large number of genes, thus making necessary the use of automated evaluation methods. In this study, we examine the influence of algorithmic parameters in the prediction power of a gene signature and in the selection process of the signature itself. We focus on one gene selection approach applied on a dataset of the budding yeast Saccharomyces cerevisiae, using quite different parameters and evaluate the influence on the selected signature. In particular, we adopt a recursive feature elimination process where at each step the prognostic power of the set of remaining genes is evaluated by five different classifiers, as well as by four classifier-fusions schemes. More specifically, we consider the logistic-sigmoid, kernel nearest centroid, kernel minimum squared error, kernel subspace, and support vector machines as classifiers with different parameters and/or kernel functions. We also study four fusion methods in order to reduce uncertainties related to the classifier evaluating the prognostic significance of genes. In all cases, the selection process is embedded into a cross validation scheme in order to enhance the confidence on the generalization of results. We consider the differences of signatures based on gene overlap and also the biological annotation of selected genes, using the MIPS FunCat architecture. We found out that a robust identification of a number of highly differential genes can offer “good” predictive power to the models. Furthermore, the classification accuracy achieved by mixtures of experts can be significantly better than the one of the individual classifiers. We also pointed out that different selection schemes result in a diverse size of gene signature, with differences in the selected genes. Nevertheless, when we annotate the genes of each signature we find that the same biological processes are invoked, with possibly small differences in the relati- e frequency of participation.