Title: MiRFFS: a functional group-based feature selection method for the identification of microRNA biomarkers
Authors: Yang Yang; Yiqun Xiao; Tianyu Cao; Wei Kong
Addresses: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China ' Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ' Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ' College of information Engineering, Shanghai Maritime University, Shanghai 201306, China
Abstract: The identification of microRNA biomarkers has been a central task in disease diagnosis, prognosis assessment and drug design. Both statistical methods and machine learning approaches have been applied to the identification of biomarkers. Especially, feature selection and regularisation techniques are efficient for filtering informative attributes from a high-dimensional space. In order to enhance their performance, the intrinsic data structure is usually exploited. In this study, we utilise the GO-based semantic similarity to infer miRNA functional groups, and propose a new feature selection method, called MiRFFS (MiRNA Functional group-based Feature Selection). We also incorporate the functional group information to the sparse group Lasso (SGL), and compare MiRFFS with SGL as well as the state-of-the-art feature selection methods. Experimental results on five miRNA microarray profiles of breast cancer show that MiRFFS can achieve a compact feature subset with substantial improvement on the accuracy compared with other feature selection and lasso methods.
Keywords: microRNA biomarker; functional group; feature selection; breast cancer.
International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.1, pp.40 - 55
Received: 06 May 2017
Accepted: 06 May 2017
Published online: 17 Aug 2017 *