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Issue No. 03 - May-June (2012 vol. 9)
ISSN: 1545-5963
pp: 799-808
G. Armano , Dept. of Electr. & Electron. Eng., Univ. of Cagliari, Cagliari, Italy
F. Ledda , Dept. of Electr. & Electron. Eng., Univ. of Cagliari, Cagliari, Italy
Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel perspective, in which understanding how available information sources are dealt with plays a central role. After revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which sources of information have been considered and which have not), we propose a generic software architecture designed to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with the proposed generic architecture has been implemented and compared with several state-of-the-art secondary structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm the validity of the approach. The predictor is available at through the corresponding web application or as downloadable stand-alone portable unpack-and-run bundle.
proteins, bioinformatics, information systems, Internet, molecular biophysics, downloadable stand-alone portable unpack-run bundle, exploiting intrastructure information, secondary structure prediction, multifaceted pipelines, proteins, bioinformatic tasks, tertiary structure prediction, sequence encoding schemes, generic software architecture, generic architecture, web application, Encoding, Correlation, Proteins, Pipelines, Amino acids, Computer architecture, Prediction algorithms, artificial neural networks., Secondary structure prediction, protein encoding schemes, ensemble architectures

G. Armano and F. Ledda, "Exploiting Intrastructure Information for Secondary Structure Prediction with Multifaceted Pipelines," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. , pp. 799-808, 2012.
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