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dc.contributor.authorKoenig, Bretteng
dc.contributor.authorKorkin, Dmitryeng
dc.contributor.corporatenameUniversity of Missouri-Columbia. Office of Undergraduate Researcheng
dc.contributor.meetingnameUndergraduate Research and Creative Achievements Forum (2008 : University of Missouri--Columbia)eng
dc.date2008eng
dc.date.issued2008eng
dc.descriptionAbstract only availableeng
dc.description.abstractThe main objective is to use three state of the art machine learning methods to find the most efficient way for predicting and characterizing the superhelical proteins based solely on their sequence information. To achieve this goal we will first apply each method individually and then integrate all three methods to obtain the most efficient and accurate prediction. We first apply the support vector machine (SVM), a feature-based approach that requires training on the set of positive and negative examples. As the second method, we will use the Bayesian inference approach. Finally, we will employ the Hidden Markov Model (HMM), another popular machine learning technique widely used in bioinformatics. Expectations for the research are that combining all three learning methods at different prediction stages will result in superior performance and accuracy when compared to each individual approach.eng
dc.description.sponsorshipCollege of Engineering Undergraduate Research Optioneng
dc.identifier.urihttp://hdl.handle.net/10355/1936eng
dc.languageen_USeng
dc.publisherUniversity of Missouri--Columbia. Office of Undergraduate Researcheng
dc.relation.ispartof2008 Undergraduate Research and Creative Achievements Forum (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Office of Undergraduate Research. Undergraduate Research and Creative Achievements Forumeng
dc.subjectsequence informationeng
dc.subjectsupport vector machine (SVM)eng
dc.subjectBayesian inference approacheng
dc.subjectHidden Markov Model (HMM)eng
dc.titlePrediction of superhelical proteins using machine learning methods [abstract]eng
dc.typePresentationeng


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