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Design and training of a neural network for predicting the solvent accessibility of proteins

✍ Scribed by Shandar Ahmad; M. Michael Gromiha

Publisher: John Wiley and Sons
Year: 2003
Tongue: English
Weight: 69 KB
Volume: 24
Category: Article
ISSN: 0192-8651
DOI: 10.1002/jcc.10298

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✦ Synopsis

Abstract

A feed‐forward neural network has been developed to predict the solvent accessibility/accessible surface area (ASA) of proteins using improved design and training methods. Several network issues ranging from the coding of ASA states to the problem of local minima of learning curve, have been addressed. Successful new approaches to overcome these problems are presented. Set of trained network weights for each ASA threshold is provided. It has been established that the prediction accuracy results with neural network are better than other reported results of ASA prediction, despite a high test to training data ratio. © 2003 Wiley Periodicals, Inc. J Comput Chem 11: 1313–1320, 2003

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