We are happy to announce that the winner of the 2019 Data Science Award is UBC Bioinformatics M.Sc. student Rebecca Asiimwe.
The Data Science Award is offered annually to an undergraduate or graduate student who has demonstrated initiative and creativity in making outstanding contributions in the field of Data Science. The initiative and creativity Rebecca has shown are certainly noteworthy. Rebecca’s nominator praised Rebecca’s enthusiasm and creativity in integrating data science with Bioinformatics to come up with new tools for analyzing and visualizing genomic data. More about Rebecca’s outstanding contributions to the field of Data Science is in her appended biography.
To read about past winners, please see: https://www.stat.ubc.ca/data-science-award.
To support this award, please see: https://bit.ly/2G7TNNz.
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Rebecca's biography
Rebecca has been working in the Shah Lab of Computational Cancer Biology at the BC Cancer Research Centre since August 2016. Her notable contributions to data science are exhibited through her research where she applied and integrated cutting-edge aspects of data science for big genomic data structuring, modeling, optimal storage, statistical analysis and visualization. Her skill set and knowledge on various programming languages, statistical computing and analytics tools, shell scripting, and database management systems went a long way in helping her develop various tools for genomic data management, exploration, analysis, and visualization.
Among the novel tools she developed is Genome-Miner, which helps research teams query, analyze, and visualize large-scale whole genome profiling data (at the level of genome-wide individual somatic variants—copy number aberrations (CNAs), single nucleotide variants (SNVs), structural variants (SVs), and insertions/deletions (indels)), using interactive and intuitive plots to understand patterns of mutations and genomic events underpinning a patient’s disease. Genome-Miner helps researchers generate novel insights and hypotheses from the data and conduct population level aggregation analysis and gene-mutation visualizations. This tool also discovers patient subgroups and helps researchers identify subgroup-specific clinically actionable events and patients most likely to respond to specific modalities. Furthermore, research teams are able to upload datasets of genomic variants and trigger analyses of interest.
A manuscript describing Rebecca’s work is in the process of publication.