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Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory

Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory

Saad O.A. Subair, Safaai Deris
Copyright: © 2007 |Pages: 26
ISBN13: 9781599042657|ISBN10: 1599042657|ISBN13 Softcover: 9781599042664|EISBN13: 9781599042671
DOI: 10.4018/978-1-59904-265-7.ch015
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MLA

Subair, Saad O.A., and Safaai Deris. "Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory." Application of Agents and Intelligent Information Technologies, edited by Vijayan Sugumaran, IGI Global, 2007, pp. 335-360. https://doi.org/10.4018/978-1-59904-265-7.ch015

APA

Subair, S. O. & Deris, S. (2007). Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory. In V. Sugumaran (Ed.), Application of Agents and Intelligent Information Technologies (pp. 335-360). IGI Global. https://doi.org/10.4018/978-1-59904-265-7.ch015

Chicago

Subair, Saad O.A., and Safaai Deris. "Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory." In Application of Agents and Intelligent Information Technologies, edited by Vijayan Sugumaran, 335-360. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-265-7.ch015

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Abstract

Protein secondary-structure prediction is a fundamental step in determining the 3D structure of a protein. In this chapter, a new method for predicting protein secondary structure from amino-acid sequences has been proposed and implemented. Cuff and Barton 513 protein data set is used in training and testing the prediction methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GOR-V information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary-structures classes (i.e., helices, strands, and coils). The newly developed method (NN-GORV-I) is further improved by applying a filtering mechanism to the searched database and hence named NN-GORV-II. The developed prediction methods are rigorously analyzed and tested together with the other five well-known prediction methods in this domain to allow easy comparison and clear conclusions.

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