Kernel-based method for feature selection and disease diagnosis using transcriptomics data

Abstract

Global transcriptome profiling is the foundation of systems biology and has been extensively used in biomarker discovery. Tools have been developed to extract meaningful biological information and useful gene features from transcriptomics data. However, there is no commonly accepted method for such purposes. The first IMPROVER (industrial methodology for process verification of research) challenge was launched to assess and verify classification methods using transcriptomics data from clinical samples. We established a computational approach that combined a kernel Fisher discriminant classifier and a feature selection scheme, which used scaled alignment selection and recursive feature elimination methods. A simple and reliable batch effect correction approach was also used. With this approach, a set of informative genes, i.e., biomarker candidates, could be identified for disease diagnosis and classification. We applied this approach to the sbv IMPROVER Challenge and achieved the highest rank in the psoriasis sub-challenge. Here, we describe our methodology and results for the sub-challenge.