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dc.contributor.advisorBajic, Vladimir B.
dc.contributor.authorBahabri, Rihab R.
dc.date.accessioned2013-06-12T13:45:49Z
dc.date.available2013-06-12T13:45:49Z
dc.date.issued2013-06
dc.identifier.doi10.25781/KAUST-WY88B
dc.identifier.urihttp://hdl.handle.net/10754/293891
dc.description.abstractActivities of DNA are to a great extent controlled epigenetically through the internal struc- ture of chromatin. This structure is dynamic and is influenced by different modifications of histone proteins. Various combinations of epigenetic modification of histones pinpoint to different functional regions of the DNA determining the so-called chromatin states. How- ever, the characterization of chromatin states by the DNA sequence properties remains largely unknown. In this study we aim to explore whether DNA sequence patterns in the human genome can characterize different chromatin states. Using DNA sequence motifs we built binary classifiers for each chromatic state to eval- uate whether a given genomic sequence is a good candidate for belonging to a particular chromatin state. Of four classification algorithms (C4.5, Naive Bayes, Random Forest, and SVM) used for this purpose, the decision tree based classifiers (C4.5 and Random Forest) yielded best results among those we evaluated. Our results suggest that in general these models lack sufficient predictive power, although for four chromatin states (insulators, het- erochromatin, and two types of copy number variation) we found that presence of certain motifs in DNA sequences does imply an increased probability that such a sequence is one of these chromatin states.
dc.language.isoen
dc.subjectchromatin
dc.subjectmachine learning
dc.subjectmotifs
dc.titlePREDICTION OF CHROMATIN STATES USING DNA SEQUENCE PROPERTIES
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberGao, Xin
dc.contributor.committeememberMoshkov, Mikhail
thesis.degree.disciplineComputer Science
thesis.degree.nameMaster of Science
refterms.dateFOA2014-06-11T00:00:00Z


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