Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00196257" target="_blank" >RIV/68407700:21230/12:00196257 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning
Original language description
We apply relational machine learning techniques to predict antimicrobial activity of peptides. We follow our successful strategy (Szabóová et al., MLSB 2010) to prediction of DNA-binding propensity of proteins from structural features. We exploit structure prediction methods to obtain peptides' spatial structures, then we construct the structural relational features. We use these relational features as attributes in a regression model. We apply this methodology to antimicrobial activity prediction of peptides achieving better predictive accuracies than a state-of-the-art approach.
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F12%2F2032" target="_blank" >GAP202/12/2032: Predicting protein properties through spatial statistical relational machine learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 2012 IEEE International Conference on Bioinformatics and Biomedicine
ISBN
978-1-4673-2558-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
575-580
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Philadelphia
Event date
Oct 4, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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