The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory
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.
Related Content
|
Rafael MartÃ, Juan-José Pantrigo, Abraham Duarte, Vicente Campos, Fred Glover.
© 2013.
21 pages.
|
|
Peng-Yeng Yin, Fred Glover, Manuel Laguna, Jia-Xian Zhu.
© 2013.
20 pages.
|
|
Volodymyr P. Shylo, Oleg V. Shylo.
© 2013.
10 pages.
|
|
Tabitha James, Cesar Rego.
© 2013.
19 pages.
|
|
Gary G. Yen, Wen-Fung Leong.
© 2013.
25 pages.
|
|
Shi Cheng, Yuhui Shi, Quande Qin.
© 2013.
29 pages.
|
|
Xin-She Yang.
© 2013.
12 pages.
|
|
|