Multiclass classification of sarcomas using pathway based feature selection method

Feature selection is an important research topic in bioinformatics, to date a large number of methods have been developed. Recently several pathway based feature selection protocols, such as the condition-responsive genes method, have been proposed for better classification performance. However, these conventional pathway based methods may lead to the selection of relevant but redundant genes in a given pathway while missing the other useful genes. Also these methods were limited to binary classification, while in many clinical problems a multiclass protocol is preferred such as the classification of sarcomas. Here, we propose a new pathway based feature selection method named Redundancy Removable Pathway based feature selection method (RRP) for the binary and multiclass classification problems. Three classifiers were implemented to compare the performance and gene functions of gene-based, conventional pathway based, and our RRP method. The validation results suggest that the RRP method is a feasible and robust feature selection method for multi-class prediction problems.