Prediction of antimicrobial peptides using hyperparameter optimized support vector machines

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dc.contributor.advisorVladimir, Bajic
dc.contributor.advisorChristoffels, Alan
dc.contributor.authorGabere, Musa Nur
dc.contributor.otherSouth African National Bioinformatics Institute (SANBI)
dc.contributor.otherFaculty of Science
dc.date.accessioned2014-01-23T07:42:16Z
dc.date.accessioned2024-05-17T07:57:56Z
dc.date.available2012/03/02 12:38
dc.date.available2012/03/02
dc.date.available2014-01-23T07:42:16Z
dc.date.available2024-05-17T07:57:56Z
dc.date.issued2011
dc.descriptionPhilosophiae Doctor - PhDen_US
dc.description.abstractAntimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae.en_US
dc.description.countrySouth Africa
dc.identifier.urihttps://hdl.handle.net/10566/15285
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectAntimicrobial peptidesen_US
dc.subjectInnate immuneen_US
dc.subjectMachine learningen_US
dc.subjectPattern searchen_US
dc.subjectSimulated annealingen_US
dc.subjectSupport vector machineen_US
dc.subjectGlobal optimizationen_US
dc.subjectDatabaseen_US
dc.subjectInsecten_US
dc.subjectGlossina morsistanen_US
dc.titlePrediction of antimicrobial peptides using hyperparameter optimized support vector machinesen_US
dc.typeThesisen_US

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Philosophiae Doctor - PhD (Bioinformatics)