NN-PRED: A novel consensus secondary structure prediction program using neural networks
In this paper, a new program for protein secondary structure prediction is proposed. The program, which is called NN-Pred, allows multiple sequences to be submitted and it returns predictions from fve secondary structure prediction algorithms. In addition, NN-Pred calculates a consensus prediction,...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Universidad Industrial de Santander
2013-03-01
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Series: | Revista UIS Ingenierías |
Subjects: | |
Online Access: | https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/3711 |
Summary: | In this paper, a new program for protein secondary structure prediction is proposed. The program, which is called NN-Pred, allows multiple sequences to be submitted and it returns predictions from fve secondary structure prediction algorithms. In addition, NN-Pred calculates a consensus prediction, which is based on a neural network strategy that is used in this paper to improve the prediction accuracy. NN-Pred was obtained by using a methodology called consensus strategy, which tries to make a better prediction by integrating some of the most remarkable existing techniques. The NN-Pred program provides a three-state (alpha-helix, beta-sheet, and other) prediction of secondary structure. According to the test sets, the prediction accuracy of NN-Pred is at least 70%, surpassing most of the existing methods. The experiments showed that neural networks can be used as a consensus strategy to producing accurate models for protein secondary prediction. |
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ISSN: | 1657-4583 2145-8456 |