Conference paper
Protein structural class prediction using support vector machine
International Conference on Electrical & Computer Engineering (ICECE 2010)
International Conference on Electrical and Computer Engineering (ICECE) 2010 (Dhaka, Bangladesh, 18/12/2010–20/12/2010)
2010
Abstract
Protein structural class prediction can play a vital role in protein 3-D structure prediction by reducing the search space of 3-D structure prediction algorithms. In this paper we used support vector machine to predict protein structural class solely based of its amino acid sequences, i.e. mainly α, mainly β, α- β and fss from CATH protein structure database; all-α, all-β, α/β, α+β from SCOP protein structure database. Four different datasets were used in this paper among them two were constructed using a unique way called Representative Protein Extraction method. During the training phase for the binary classification 99.91% accuracy was achieved for fss vs. others. Also during the testing phase for SCOP database the overall prediction accuracy was 97.14% whereas for CATH database it was 96%. The results obtained in this study are quite encouraging, indicating that it can be used as a complimentary method for protein class prediction to many other existing methods.
Details
- Title
- Protein structural class prediction using support vector machine
- Authors/Creators
- GM. Shafiullah (Author/Creator)H.A. Al-Mamun (Author/Creator) - Islamic University of Technology
- Publication Details
- International Conference on Electrical & Computer Engineering (ICECE 2010)
- Conference
- International Conference on Electrical and Computer Engineering (ICECE) 2010 (Dhaka, Bangladesh, 18/12/2010–20/12/2010)
- Identifiers
- 991005542596107891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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