Protein Secondary Structure Prediction by Machine Learning Methods
| Authors | |
|---|---|
| Year of publication | 2005 |
| Type | Article in Proceedings |
| Conference | 1st International Summer School on Computational Biology |
| MU Faculty or unit | |
| 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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