Making Sense of Biological Big Data using Julia

For the December meetup we have Marc Williams from UCL talking on the research work he is doing on Bayesian computation, Big Data and biological data and the role Julia plays in the work.

Synopsis:

The amount of biological data being generated has exploded in the past 5 years, primarily due to the decreasing costs of high throughput sequencing. In particular tens of thousands of cancer genomes have been sequenced. Interpreting this data in a meaningful way however, is challenging. We hypothesized that the patterns of genetic changes in cancers genomes that we observe are a consequence of how cancers grow. To test this we developed a stochastic simulation of tumor growth, which we could fit to cancer genomic data using the Approximate Bayesian Computation (ABC) framework.

Due to Julia’s unique capabilities we implemented our stochastic simulations as well as the statistical inference in the language. Approximate Bayesian methods require potentially millions of simulations with different parameters to accurately fit to data, Julia’s speed and ease of use makes it an attractive option for such problems. Marc will also touch on why Julia holds huge promise for other “big data” problems and how he’s been using Julia as an open research tool with other projects such as Jupyter notebooks.

Marc Williams is a 3rd year PhD student at the Barts Cancer Institute and University College London. Originally trained in physics, he now applies mathematical modelling techniques to understand cancer through the lens of evolutionary biology. His work involves mathematical and computational modelling together with Bayesian inference techniques and analysis of large genomic datasets.

As is usual we would like to thank Skillsmatter for the venue and please note that SKM would like attendees to register with them as well as RSVP'ing here, using the URL=> https://skillsmatter.com/meetups/8776-london-julia-users-group