title: Scatter-search with support vector machine for prediction of relative solvent accessibility.
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.
authors.affiliation: Young researchers Club, South Tehran Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Amir Hosein
authors.initials: AH
authors.lastname: Kashefi
authors.affiliation: Department of Computer Engineering, Islamic Azad University, Damavand Branch, Damavand, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Meshkin
authors.affiliation: Department of Computer Engineering, Islamic Azad University, Damavand Branch, Damavand, Iran.
authors.forename: Mina
authors.initials: M
authors.lastname: Zargoosh
authors.affiliation: Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran.
authors.forename: Javad
authors.initials: J
authors.lastname: Zahiri
authors.affiliation: Department of Computer Engineering, Islamic Azad University, Damavand Branch, Damavand, Iran.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Taheri
authors.affiliation: Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran.
authors.forename: Saman
authors.initials: S
authors.lastname: Ashtiani
keywords: PSI-BLAST; evolutionary information; feature selection methods; physicochemical properties of amino acids; support vector regression