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2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (2012)
Philadelphia, USA USA
Oct. 4, 2012 to Oct. 7, 2012
ISBN: 978-1-4673-2746-6
pp: 575-580
Andrea Szaboova , Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
Ondrej Kuzelka , Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
Filip Zelezny , Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
ABSTRACT
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.
INDEX TERMS
data mining, Antimicrobial activity prediction, peptides, relational machine learning
CITATION

A. Szaboova, O. Kuzelka and F. Zelezny, "Prediction of antimicrobial activity of peptides using relational machine learning," 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops(BIBMW), Philadelphia, USA USA, 2012, pp. 575-580.
doi:10.1109/BIBMW.2012.6470203
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