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Article

Prediction of secondary protein structure content from primary sequence alone – a feature selection based approach

Authors:

Leila HomaeianAuthors Info & Claims

MLDM'05: Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition

Pages 334 - 345

https://doi.org/10.1007/11510888_33

Published: 09 July 2005 Publication History

Abstract

Research in protein structure and function is one of the most important subjects in modern bioinformatics and computational biology. It often uses advanced data mining and machine learning methodologies to perform prediction or pattern recognition tasks. This paper describes a new method for prediction of protein secondary structure content based on feature selection and multiple linear regression. The method develops a novel representation of primary protein sequences based on a large set of 495 features. The feature selection task performed using very large set of nearly 6,000 proteins, and tests performed on standard non-homologues protein sets confirm high quality of the developed solution. The application of feature selection and the novel representation resulted in 14-15% error rate reduction when compared to results achieved when standard representation is used. The prediction tests also show that a small set of 5-25 features is sufficient to achieve accurate prediction for both helix and strand content for non-homologous proteins.

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Cited By

Dehzangi APaliwal KSharma ADehzangi OSattar A(2013)A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction ProblemIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.6510:3(564-575)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1109/TCBB.2013.65
Altun GHu HGremalschi SHarrison RPan Y(2007)A feature selection algorithm based on graph theory and random forests for protein secondary structure predictionProceedings of the 3rd international conference on Bioinformatics research and applications10.5555/1759681.1759735(590-600)Online publication date: 7-May-2007
https://dl.acm.org/doi/10.5555/1759681.1759735
Kurgan LHomaeian L(2006)Prediction of structural classes for protein sequences and domains-Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracyPattern Recognition10.1016/j.patcog.2006.02.01439:12(2323-2343)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.1016/j.patcog.2006.02.014

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Information & Contributors

Information

Published In

cover image Guide Proceedings

MLDM'05: Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition

July 2005

691 pages

ISBN:3540269231

Editors:
Petra Perner
Institute of Computer Vision and applied Computer Sciences, IBaI, Germany
,
Atsushi Imiya
Institute of Media and Information Technology, Chiba University, Japan

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 09 July 2005

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Cited By

Dehzangi APaliwal KSharma ADehzangi OSattar A(2013)A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction ProblemIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2013.6510:3(564-575)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1109/TCBB.2013.65
Altun GHu HGremalschi SHarrison RPan Y(2007)A feature selection algorithm based on graph theory and random forests for protein secondary structure predictionProceedings of the 3rd international conference on Bioinformatics research and applications10.5555/1759681.1759735(590-600)Online publication date: 7-May-2007
https://dl.acm.org/doi/10.5555/1759681.1759735
Kurgan LHomaeian L(2006)Prediction of structural classes for protein sequences and domains-Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracyPattern Recognition10.1016/j.patcog.2006.02.01439:12(2323-2343)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.1016/j.patcog.2006.02.014

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