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.
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