Genome-wide studies are often carried out today to identify connections between genomic features and observable traits.
They are of interest to medical scientists and biologists because they can help identify the genomic components of diseases such as cancer. But they also challenge, and therefore fascinate, mathematical statisticians, not least because they involve many more 'features' (eg, genes or fragments of DNA) than there are independent sources of information (eg, people in a sample). While data on genomic variation clearly lie at the heart of this work, mathematical methods and computational algorithms for implementing them are needed to make sense of the data.
The symposium will bring together scientists working on both genomic and mathematical aspects of these problems, so that each area might learn about the progress of the other, and to identify new frontiers in this rapidly evolving area.
Genome-wide studies are often carried out today to identify connections between genomic features and observable traits.
They are of interest to medical scientists and biologists because they can help identify the genomic components of diseases such as cancer. But they also challenge, and therefore fascinate, mathematical statisticians, not least because they involve many more 'features' (eg, genes or fragments of DNA) than there are independent sources of information (eg, people in a sample). While data on genomic variation clearly lie at the heart of this work, mathematical methods and computational algorithms for implementing them are needed to make sense of the data.
The symposium will bring together scientists working on both genomic and mathematical aspects of these problems, so that each area might learn about the progress of the other, and to identify new frontiers in this rapidly evolving area.
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