A new feature selection strategy for K-mers sequence representation

Abstract

DNA sequence decomposition into k-mers (substrings of length k) and their frequency counting, defines a mapping of a sequence into a numerical space by a numerical feature vector of fixed length. This simple process allows to compute sequence comparison in an alignment free way, using common similarities and distance functions on the numerical codomain of the mapping. The most common used decomposition uses all the substrings of length k making the codomain of exponential dimension. This obviously can affect the time complexity of the similarity computation, and in general of the machine learning algorithm used for the purpose of sequence classification. Moreover, the presence of possible noisy features can also affect seriously the classification accuracy. In this paper we propose a feature selection method able to select the most informative k-mers associated to a set of DNA sequences. Such selection is based on the Motif Independent Measure (MIM), an unbiased quantitative measure for DNA sequence specificity that we have recently introduced in the literature. Results computed on three public datasets using the Support vector machine classifier, show the effectiveness of the proposed feature selection method