Protein Secondary Structure Prediction by Machine Learning Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F05%3A00014719" target="_blank" >RIV/00216224:14330/05:00014719 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Protein Secondary Structure Prediction by Machine Learning Methods
Original language 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.
Czech name
Rozpoznavani sekundarni struktury proteinu metodami strojoveho uceni
Czech description
Clanek se zabyva aplikaci metod strojoveho uceni na problem predikce sekundarni struktury neznamych proteinu. Cilem je porovnat umele neuronove site, jako nejmodernejsi pouzivane metody, s rozhodovacimi stromy a naivnim Bayesovskym klasifikatorem. Podrobne experimenty jsou provadeny na vybranych datech z PDB databaze proteinu. Vysledky ukazuji, ze rozhodovaci stromy dosahuji mnohem lepsich vysledku (87.4%) nez neuronove site (80.5%). Oproti tomu naivni Bayesuv klasifikator se ukazal jako neprilis vhodny.
Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
1st International Summer School on Computational Biology
ISBN
80-210-3907-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
38-43
Publisher name
Masaryk University
Place of publication
Brno, Czech Republic
Event location
Brno, Czech Republic
Event date
Sep 4, 2005
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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