Data Science

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Recent genome sequencing technologies are generating unprecedented amounts of data. Unambiguous computational analysis of genomic data is of key importance for precision medicine. Here we propose novel algorithmic and data science methods that can infer important clinical information within the ambiguous parts of sequencing data in fast and cost-efficient manner. We also propose a comprehensive domain-specific framework for developing high-performance computational tools that can produce fast and scalable data processing software for different computer architectures. In collaboration with local and international research centers, we envision that the proposed methods will boost Canadian health care and research capabilities.