An MLP-based feature subset selection for HIV-1 protease cleavage site analysis

摘要

ObjectiveIn recent years, several machine learning approaches have been applied to modeling the specificity of the human immunodeficiency virus type 1 (HIV-1) protease cleavage domain. However, the high dimensional domain dataset contains a small number of samples, which could misguide classification modeling and its interpretation. Appropriate feature selection can alleviate the problem by eliminating irrelevant and redundant features, and thus improve prediction performance.