Improved performance in protein secondary structure prediction by combining multiple predictions.

Abstract:

:In this paper(1) we present a novel framework for protein secondary structure prediction. In this prediction framework, firstly we propose a novel parameterized semi-probability profile, which combines single sequence with evolutionary information effectively. Secondly, different semi-probability profiles are respectively applied as network input to predict protein secondary structure. Then a comparison among these different predictions is discussed in this article. Finally, naïve Bayes approaches are used to combine these predictions in order to obtain a better prediction performance than individual prediction. The experimental results show that our proposed framework can indeed improve the prediction accuracy.

journal_name

Protein Pept Lett

authors

Huang DS,Huang X

doi

10.2174/092986606778777551

subject

Has Abstract

pub_date

2006-01-01 00:00:00

pages

985-91

issue

10

eissn

0929-8665

issn

1875-5305

journal_volume

13

pub_type

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