A Framework for Predicting Proteins 3D Structures

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contributor.author Duwairi, Rehab en_US
contributor.author Kassawneh, Amal en_US
date.accessioned 2009-12-28T06:59:30Z en_US
date.available 2009-12-28T06:59:30Z en_US
date.issued 2008-04-22 en_US
identifier.citation Duwairi, R.; Kassawneh, A., "A framework for predicting proteins 3D structures," Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on , vol., no., pp.37-44, March 31 2008-April 4 2008 en_US
identifier.uri http://dx.doi.org/10.1109/AICCSA.2008.4493514 en_US
identifier.uri http://hdl.handle.net/10576/10506 en_US
description.abstract This paper proposes a framework for predicting protein three dimensional structures from their primary sequences. The proposed method utilizes the natural multi-label and hierarchical intrinsic nature of proteins to build a multi-label and hierarchical classifier for predicting protein folds. The classifier predicts protein folds in two stages, at the first stage, it predicts the protein structural class, and in the second stage, it predicts the protein fold. When comparing our technique with SVM, naive Bayes, and boosted C4.5 we get a higher accuracy more than SVM and better than naive Bayes when using the composition, secondary structure and hydrophobicity feature attributes, and give higher accuracy than C4.5 when using composition, secondary structure, hydrophobicity, and polarity feature attributes. MuLAM was used as a basic classifier in the hierarchy of the implemented framework. Two major modifications were made to MuLAM, namely: the pheromone update and term selection strategies of MuLAM were altered. en_US
language.iso en en_US
publisher IEEE en
subject predicting proteins 3D structures en_US
subject Bayes methods en
subject biology computing en
subject molecular biophysics en
subject pattern classification en
subject proteins en
subject support vector machines en
title A Framework for Predicting Proteins 3D Structures en_US
type Article en_US


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