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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Kurgan, L., Homaeian, L. (2005). Prediction of Secondary Protein Structure Content from Primary Sequence Alone – A Feature Selection Based Approach. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_33
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DOI: https://doi.org/10.1007/11510888_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
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