Authors: Ashtiani, Saman
Kashefi, Amir Hosein
Meshkin, Alireza
Taheri, Mohsen
Zahiri, Javad
Zargoosh, Mina
Title: Scatter-search with support vector machine for prediction of relative solvent accessibility
Language (ISO): en
Abstract: Proteins have vital roles in the living cells. The protein function is almost completely dependent on protein structure. The prediction of relative solvent accessibility gives helpful information for the prediction of tertiary structure of a protein. In recent years several relative solvent accessibility (RSA) prediction methods including those that generate real values and those that predict discrete states have been developed. The proposed method consists of two main steps: the first one, provided subset selection of quantitative features based on selected qualitative features and the second, dedicated to train a model with selected quantitative features for RSA prediction. The results show that the proposed method has an improvement in average prediction accuracy and training time. The proposed method can dig out all the valuable knowledge about which physicochemical features of amino acids are deemed more important in prediction of RSA without human supervision, which is of great importance for biologists and their future researches.
Subject Headings: evolutionary information
feature selection methods
physicochemical properties of amino acids
support vector regression
Issue Date: 2013-05-16
Appears in Collections:Original Articles

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