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@inproceedings{619864,
author = {Hroza, Jiří},
address = {Brno, Czech Republic},
booktitle = {1st International Summer School on Computational Biology},
keywords = {machine learning; protein; protein secondary structure prediction},
language = {eng},
location = {Brno, Czech Republic},
isbn = {80-210-3907-8},
pages = {38-43},
publisher = {Masaryk University},
title = {Protein Secondary Structure Prediction by Machine Learning Methods},
year = {2005}
}
TY - JOUR
ID - 619864
AU - Hroza, Jiří
PY - 2005
TI - Protein Secondary Structure Prediction by Machine Learning Methods
PB - Masaryk University
CY - Brno, Czech Republic
SN - 8021039078
KW - machine learning
KW - protein
KW - protein secondary structure prediction
N2 - 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.
ER -
HROZA, Jiří. Protein Secondary Structure Prediction by Machine Learning Methods. In \textit{1st International Summer School on Computational Biology}. Brno, Czech Republic: Masaryk University, 2005, s.~38-43, 5 s. ISBN~80-210-3907-8.
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