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AuthorDuwairi, Rehab
AuthorKassawneh, Amal
Available date2009-12-28T06:59:30Z
Publication Date2008-04-22
Publication NameIEEE/ACS International Conference on Computer Systems and Applications 2008
CitationDuwairi, 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
AbstractThis 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.
Subjectpredicting proteins 3D structures
SubjectBayes methods
Subjectbiology computing
Subjectmolecular biophysics
Subjectpattern classification
Subjectsupport vector machines
TitleA Framework for Predicting Proteins 3D Structures
TypeConference Paper

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