Prediction of relative solvent accessibility by support vector regression and best-first method
Loading...
Date
2010-02-09T16:01:20Z
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Since, it is believed that the native structure of most proteins is defined by their sequences, utilizing data mining methods to extract hidden knowledge from protein sequences, are unavoidable.
A major difficulty in mining bioinformatics data is due to the size of the datasets
which contain frequently large numbers of variables. In this study, a two-step procedure for prediction of relative solvent accessibility of proteins is presented. In a first “feature selection” step, a small subset of evolutionary information is identified on the basis of selected
physicochemical properties. In the second step, support vector regression is used to real value prediction of protein solvent accessibility with these custom selected features of evolutionary information. The experiment results show that the proposed method is an improvement in average
prediction accuracy and training time.
Description
Table of contents
Keywords
Feature selection method, physicochemical properties of amino acids, PSI-BLAST, support vector regression