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dc.contributor.authorChang, Yi Tanen_US
dc.date.accessioned2018-06-30T08:01:52Z
dc.date.available2018-06-30T08:01:52Z
dc.date.issued2018-06-29en_US
dc.identifier.othervt_gsexam:15161en_US
dc.identifier.urihttp://hdl.handle.net/10919/83813
dc.description.abstractThis thesis consists of two projects in which various machine learning approaches and statistical analysis for the integration of biomedical data analysis were explored, developed and tested. Integration of different biomedical data sources allows us to get a better understating of human body from a bigger picture. If we can get a more complete view of the data, we not only get a more complete view of the molecule basis of phenotype, but also possibly can identify abnormality in diseases which were not found when using only one type of biomedical data. The objective of the first project is to find biological pathways which are related to Duechenne Muscular Dystrophy(DMD) and Lamin A/C(LMNA) using the integration of multi-omics data. We proposed a novel method which allows us to integrate proteins, mRNAs and miRNAs to find disease related pathways. The goal of the second project is to develop a personalized recommendation system which recommend cancer treatments to patients. Compared to the traditional way of using only users' rating to impute missing values, we proposed a method to incorporate users' profile to help enhance the accuracy of the prediction.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectData integrationen_US
dc.subjectmachine learningen_US
dc.subjectpathway enrichmenten_US
dc.subjectpathway prioritizationen_US
dc.subjectmatrix completionen_US
dc.subjecttreatment recommendation.en_US
dc.titleA Study of Machine Learning Approaches for Integrated Biomedical Data Analysisen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairYu, Guoqiangen_US
dc.contributor.committeememberMili, Lamine M.en_US
dc.contributor.committeememberWang, Yue J.en_US


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