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Using a mixture of probabilistic decision trees for direct prediction of protein function
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Weave amino acid sequences for protein secondary structure prediction
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Artificial Intelligence in Medicine
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
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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.