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Author Duwairi, Rehaben_US
Author Kassawneh, Amalen_US
Available date 2009-12-28T06:59:30Zen_US
Publication Date 2008-04-22en_US
Publication Name IEEE/ACS International Conference on Computer Systems and Applications 2008
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 2008en_US
URI http://dx.doi.org/10.1109/AICCSA.2008.4493514en_US
URI http://hdl.handle.net/10576/10506en_US
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 enen_US
Publisher IEEEen
Subject predicting proteins 3D structuresen_US
Subject Bayes methodsen
Subject biology computingen
Subject molecular biophysicsen
Subject pattern classificationen
Subject proteinsen
Subject support vector machinesen
Title A Framework for Predicting Proteins 3D Structuresen_US
Type Conference Paperen_US


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