Evaluating biological characteristics for protein function prediction using support vector machine
by Gabriela Teodoro De Oliveira Santos; Cristiane Neri Nobre; Luis Enrique Zárate
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 17, No. 1, 2021

Abstract: Predicting protein function is a latent problem and a challenge in the field of Bioinformatics. Over the years, several computational approaches have been proposed for this purpose. One of the approaches is based on characteristics, which makes use of biologic relevant information. The several contributions with this approach have considered a set of characteristics belonging to the four protein structures to predict the class and function of proteins. In this study, we evaluate several sets of characteristics that represent the four structural levels (primary, secondary, tertiary and quaternary), such as electrostatic potential, hydrophobicity, amino acids frequency, distance between α-carbons, and molecular weight. In order to evaluate the relevance of the characteristics, we employed the support vector machine (SVM) classifier, which usually presents satisfactory results in the process of biological data classification. The objective of this study is to contribute for the most appropriate choice of characteristics for the protein function prediction problem.

Online publication date: Tue, 06-Apr-2021

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