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
收藏
页码：731 / 739
页数：9
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