Data Science

最近的基因组测序技术正在产生前所未有的数据量。基因组数据的明确计算分析是精确医学的关键。在这里，我们提出了新的算法和数据科学方法，可以快速和经济高效地在测序数据的模糊部分推断重要的临床信息。我们还提出了一个全面的特定于领域的框架来开发高性能计算工具，该工具可以为不同的计算机体系结构生成快速和可扩展的数据处理软件。通过与当地和国际研究中心的合作，我们设想拟议的方法将提高加拿大的医疗保健和研究能力。

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.