Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

ABSTRACT: It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1', and p2' are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and imp

SUBMITTER: Liu H 

PROVIDER: S-EPMC4413510 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature