Time efficient and novel ways of analyzing high-dimensional multi-omics datasets: parallel computing and multi-view learning

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dc.contributor.authorShikder, Rayhan
dc.contributor.examiningcommitteeLeung, Carson Kai-Sang (Computer Science)en_US
dc.contributor.examiningcommitteeMcLeod, Robert D. (Electrical & Computer Engineering)en_US
dc.contributor.supervisorHu, Pingzhao (Biochemistry & Medical Genetics) Irani, Pourang (Computer Science)en_US
dc.date.accessioned2019-08-08T20:20:25Z
dc.date.available2019-08-08T20:20:25Z
dc.date.issued2019-07-26en_US
dc.date.submitted2019-08-07T20:48:23Zen
dc.date.submitted2019-08-08T20:09:00Zen
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractDue to the advancements in high-throughput sequencing technologies high- dimensional omics data are rapidly increasing in number and require enhanced computational power to make sense. On the other hand, an integrated analysis of these omics data has the potential to reveal a comprehensive picture of any biological phenomenon than analyzing them separately (one omics data at a time). This thesis has two main contributions in the field of computer science and bioinformatics. First, the development of new CPU-based improved parallel algorithms for finding the LCS of DNA sequence data. Second, the thorough investigation of the existing canonical correlation analysis (CCA)-based multi-view learning methods for analyzing multi-omics data along with developing a new multi-view learning framework consisting of novel supervised DNN-based multi-view learning techniques.en_US
dc.description.noteOctober 2019en_US
dc.identifier.citationShikder, R., Thulasiraman, P., Irani, P., & Hu, P. (2019). An OpenMP-based tool for finding longest common subsequence in bioinformatics. BMC research notes, 12(1), 220.en_US
dc.identifier.citationShikder, R., Irani, P., & Hu, P. (2019, May). Genome-Wide Canonical Correlation Analysis-Based Computational Methods for Mining Information from Microbiome and Gene Expression Data. In Canadian Conference on Artificial Intelligence (pp. 511-517). Springer, Cham.en_US
dc.identifier.urihttp://hdl.handle.net/1993/34066
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectLCSen_US
dc.subjectParallel Computingen_US
dc.subjectMulti-view Learningen_US
dc.subjectCCAen_US
dc.subjectDeep Canonical Correlation Analysis (DCCA)en_US
dc.subjectMulti-omics dataen_US
dc.titleTime efficient and novel ways of analyzing high-dimensional multi-omics datasets: parallel computing and multi-view learningen_US
dc.typemaster thesisen_US
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