Abstract: “The development of a method to annotate unknown gene functions is an important task in bioinformatics. The identification of the relevant genes to metabolic pathways is also helpful for understanding the genes. However, the relationships between metabolic pathways and genes are complicated. Thus, it is difficult to identify the relevant genes by linear models. In this study, we propose a new method based on the SVM approach, for inferring the genes involved in metabolic pathways from the gene expression profiles. To improve the classification performances of SVMs, we developed a method for finding the important interactions for classification, from a huge number of experiment combinations. The interactions selected by our method were added as new features to the training data set of the SVMs. Furthermore, feature selection by the Gini importance was applied, to avoid overlearning of the SVMs. To demonstrate the validity of our method, we trained SVMs with Saccharomyces cerevisiae gene”

Keywords: metabolic pathways, gene involved in, gene expression profiles, microarray, interaction, support vector machines, SVM, Gini importance, random forests, machine learning, Saccharomyces cerevisiae