Protein Secondary Structure Prediction by Machine Learning Methods

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Authors

HROZA Jiří

Year of publication 2005
Type Article in Proceedings
Conference 1st International Summer School on Computational Biology
MU Faculty or unit

Faculty of Informatics

Citation
Field Informatics
Keywords machine learning; protein; protein secondary structure prediction
Description This paper concerns about an application of machine learning methods to a prediction of a secondary structure of an unknown protein. The aim of this study is to the compare artificial neural networks as the state of art method with decision trees and naive Bayes classifier. Detailed experiments are done on selected PDB database data. Results shows that decision trees achieving 87.4 % Q3 accuracy outperform neural networks (80.5 %). Naive Bayes classifier is unusable for this task.
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