An evolutionary approach for motif discovery and transmembrane protein classification

Tsunoda, D.F. and Lopes, H.S. and Freitas, A.A. (2005) An evolutionary approach for motif discovery and transmembrane protein classification. In: Applications of Evolutionary Computing (Proc. of EvoBIO-2005: 3rd European Workshop on Evolutionary Bioinformatics), LNCS 3449. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)

Abstract

Proteins can be grouped into families according to their biological functions. This paper presents a system, named GAMBIT, which discovers motifs (particular sequences of amino acids) that occur very often in proteins of a given family but rarely occur in proteins of other families. These motifs are used to classify unknown proteins, that is, to predict their function by analyzing the primary structure. To search for motifs in proteins, we developed a GA with specially tailored operators for the problem. GAMBIT was compared with MEME, a web tool for finding motifs in the TransMembrane Protein DataBase. Motifs found by both methods were used to build a decision tree and classification rules, using, respectively, C4.5 and Prism algorithms. Motifs found by GAMBIT led to significantly better results, when compared with those found by MEME, using both classification algorithms.

Item Type: Conference or workshop item (UNSPECIFIED)
Uncontrolled keywords: evolutionary algorithms, bioinformatics, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03
Last Modified: 28 May 2012 15:02
Resource URI: http://kar.kent.ac.uk/id/eprint/14348 (The current URI for this page, for reference purposes)
  • Depositors only (login required):