A Comparative Study on Filtering Protein Secondary Structure Prediction

被引：15

作者：

Kountouris, Petros ^{[1
]}

Agathocleous, Michalis ^{[1
]}

Promponas, Vasilis J. ^{[2
]}

Christodoulou, Georgia ^{[1
]}

Hadjicostas, Simos ^{[1
]}

Vassiliades, Vassilis ^{[1
]}

Christodoulou, Chris ^{[1
]}

机构：

[1] Univ Cyprus, Dept Comp Sci, CY-1678 Nicosia, Cyprus

[2] Univ Cyprus, Dept Biol Sci, CY-1678 Nicosia, Cyprus

来源：

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2012年 / 9卷 / 03期

关键词：

Protein secondary structure prediction; filtering; machine learning; structural bioinformatics; bidirectional recurrent neural networks; MULTIPLE LINEAR-REGRESSION; RECURRENT NEURAL-NETWORKS; DIHEDRAL ANGLES; PSI-BLAST; INFORMATION; ALGORITHM; ACCURACY; MATRICES; DATABASE; SERVER;

D O I：

10.1109/TCBB.2012.22

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：731 / 739

页数：9

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