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dc.contributor.advisorDatta, Aniruddha
dc.creatorKapoor, Rajan
dc.date.accessioned2017-08-21T14:43:54Z
dc.date.available2019-05-01T06:10:10Z
dc.date.created2017-05
dc.date.issued2017-05-08
dc.date.submittedMay 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/161604
dc.description.abstractCancer cells are much more prone to mutations than normal cells, generating over time, more genetic variants of themselves within the tissue. Drugs designed for one variant might not work as intended for other variants. As such, effective drug design requires estimation of proportion of various cancer subpopulations. In this work, a mixture model based approach with expectation maximization is proposed for determination of cancer heterogeneity. We exploit the pathway knowledge collected by biologists over time to surpass the limitations of identifiability shown by mixture models. Also in cases where Expectation-Maximization converges to more than one solution, pathway knowledge is used to break the tie by defining an error metric. Finally, using experimental data, changes in composition of the mixture over time are estimated using the model. The approach can also be used to compare the effectiveness of different drugs on a heterogeneous cancer tissue by observing the response over time.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectCancer Heterogeneityen
dc.subjectMixture Modelen
dc.subjectExpectation Maximizationen
dc.titleExpectation-Maximization Based Mixture-Model Exploiting Pathway Knowledge for Cancer Heterogeneityen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberBraga-Neto, Ulisses
dc.contributor.committeeMemberKhatri, Sunil
dc.contributor.committeeMemberMahapatra, Rabi
dc.type.materialtexten
dc.date.updated2017-08-21T14:43:54Z
local.embargo.terms2019-05-01
local.etdauthor.orcid0000-0003-4773-7002


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