Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming

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@Article{Veltri:2015:ieeeacmCBBI,
  author =       "Daniel Veltri and Uday Kamath and Amarda Shehu",
  journal =      "IEEE/ACM Transactions on Computational Biology and
                 Bioinformatics",
  title =        "Improving Recognition of Antimicrobial Peptides and
                 Target Selectivity through Machine Learning and Genetic
                 Programming",
  year =         "2015",
  abstract =     "Growing bacterial resistance to antibiotics is
                 spurring research on using naturally-occurring
                 antimicrobial peptides (AMPs) as templates for novel
                 drug design. While experimentalists mainly focus on
                 systematic point mutations to measure the effect on
                 antibacterial activity, the computational community
                 seeks to understand what determines such activity in a
                 machine learning setting. The latter seeks to identify
                 the biological signals or features that govern
                 activity. In this paper, we advance research in this
                 direction through a novel method that constructs and
                 selects complex sequence-based features which capture
                 information about distal patterns within a peptide.
                 Comparative analysis with state-of-the-art methods in
                 AMP recognition reveals our method is not only among
                 the top performers, but it also provides transparent
                 summaries of antibacterial activity at the sequence
                 level. Moreover, this paper demonstrates for the first
                 time the capability not only to recognise that a
                 peptide is an AMP or not but also to predict its target
                 selectivity based on models of activity against only
                 Gram-positive, only Gram-negative, or both types of
                 bacteria. The work described in this paper is a step
                 forward in computational research seeking to facilitate
                 AMP design or modification in the wet laboratory.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TCBB.2015.2462364",
  ISSN =         "1545-5963",
  notes =        "Daniel Veltri is with the School of Systems Biology,
                 George Mason University Fairfax, VA 22030

                 Also known as \cite{7172462}",
}

Genetic Programming entries for Daniel Veltri Uday Kamath Amarda Shehu

Citations