A Comparative Study on Filtering Protein Secondary Structure Prediction

Author

Kountouris, Petros ; Agathocleous, Michalis ; Promponas, Vasilis J. ; Christodoulou, Georgia ; Hadjicostas, Simos ; Vassiliades, Vassilis ; Christodoulou, Chris

Author_Institution

Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus

Volume

9

Issue

3

fYear

2012

Firstpage

731

Lastpage

739

Abstract

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

Keywords

bioinformatics; filtering theory; learning (artificial intelligence); molecular biophysics; molecular configurations; proteins; empirical rule; machine learning technique; protein secondary structure prediction filtering; Accuracy; Filtering; Logistics; Machine learning; Machine learning algorithms; Proteins; Training; Protein secondary structure prediction; bidirectional recurrent neural networks.; filtering; machine learning; structural bioinformatics; Animals; Artificial Intelligence; Databases, Protein; Humans; Protein Structure, Secondary; Proteins;

fLanguage

English

Journal_Title

Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Publisher

ieee

ISSN

1545-5963

Type

jour

DOI

10.1109/TCBB.2012.22

Filename

6138848