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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.
  • 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. (en)
Title
  • Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning
  • Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning (en)
skos:prefLabel
  • Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning
  • Prediction of Antimicrobial Activity of Peptides using Relational Machine Learning (en)
skos:notation
  • RIV/68407700:21230/12:00196257!RIV13-GA0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP202/12/2032)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 161021
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/12:00196257
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Relational Machine Learning; Antimicrobial Activity Prediction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [2B75EB95ECE8]
http://linked.open...v/mistoKonaniAkce
  • Philadelphia
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • Proceedings of 2012 IEEE International Conference on Bioinformatics and Biomedicine
http://linked.open...in/vavai/riv/obor
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http://linked.open...iv/tvurceVysledku
  • Kuželka, Ondřej
  • Szabóová, Andrea
  • Železný, Filip
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • IEEE Computer Society
https://schema.org/isbn
  • 978-1-4673-2558-5
http://localhost/t...ganizacniJednotka
  • 21230
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