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Bonnie Berger. Cho, Hyunghoon. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-11-04T20:21:19Z 2019-11-04T20:21:19Z 2019 2019 https://hdl.handle.net/1721.1/122727 1124678851 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Page 307 blank. Includes bibliographical references (pages 279-306). Recent advances in high-throughput experimental technologies have led to the exponential growth of biomedical datasets, including personal genomes, single-cell sequencing experiments, and molecular interaction networks. The unprecedented scale, variety, and distributed ownership of emerging biomedical datasets present key computational challenges for sharing and analyzing these data to uncover new scientific insights. This thesis introduces a range of computational methods that overcome these challenges to enable scalable sharing and analysis of massive datasets in a range of biomedical domains. First, we introduce scalable privacy-preserving analysis pipelines built upon modern cryptographic tools to enable large amounts of sensitive biomedical data to be securely pooled from multiple entities for collaborative science. Second, we introduce efficient computational techniques for analyzing emerging large-scale sequencing datasets of millions of cells that leverage a compact summary of the data to speedup various analysis tasks while maintaining the accuracy of results. Third, we introduce integrative approaches to analyzing a growing variety of molecular interaction networks from heterogeneous data sources to facilitate functional characterization of poorly-understood genes. The computational techniques we introduce for scaling essential biomedical analysis tasks to the large volume of data being generated are broadly applicable to other data science domains. by Hyunghoon Cho. Ph.D. Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 307 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Biomedical data sharing and analysis at scale : privacy, compaction, and integration Thesis application/pdf 2019-11-04T20:21:18Z Publication Doctoral EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="1dd798f7-1a7c-4350-b679-a4a2ba46dffe"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Biomedical data sharing and analysis at scale : privacy, compaction, and integration</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Cho, Hyunghoon.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Recent advances in high-throughput experimental technologies have led to the exponential growth of biomedical datasets, including personal genomes, single-cell sequencing experiments, and molecular interaction networks. The unprecedented scale, variety, and distributed ownership of emerging biomedical datasets present key computational challenges for sharing and analyzing these data to uncover new scientific insights. This thesis introduces a range of computational methods that overcome these challenges to enable scalable sharing and analysis of massive datasets in a range of biomedical domains. First, we introduce scalable privacy-preserving analysis pipelines built upon modern cryptographic tools to enable large amounts of sensitive biomedical data to be securely pooled from multiple entities for collaborative science. Second, we introduce efficient computational techniques for analyzing emerging large-scale sequencing datasets of millions of cells that leverage a compact summary of the data to speedup various analysis tasks while maintaining the accuracy of results. Third, we introduce integrative approaches to analyzing a growing variety of molecular interaction networks from heterogeneous data sources to facilitate functional characterization of poorly-understood genes. The computational techniques we introduce for scaling essential biomedical analysis tasks to the large volume of data being generated are broadly applicable to other data science domains.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>