Krein support vector machine classification of antimicrobial peptides
Joseph Redshaw, Darren Shu Jeng Ting, Alex Brown, Jonathan D. Hirst, Thomas Gärtner
Abstract
, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.
Topics & Concepts
Support vector machineAntimicrobialKernel (algebra)Antimicrobial peptidesArtificial intelligencePattern recognition (psychology)Relevance vector machineMachine learningVector (molecular biology)Computer scienceChemistryMathematicsMicrobiologyBiologyBiochemistryPure mathematicsGeneRecombinant DNAMachine Learning in BioinformaticsAntimicrobial Peptides and Activitiesvaccines and immunoinformatics approaches