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Friday, June 23 • 2:42pm - 2:54pm
Improving Biological Network Inference with Julia

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In the multi-disciplinary field of systems biology, we welcome the opportunity that Julia brings for writing fast software with simple syntax. Speed is important in an age when biological datasets are increasing in size and analyses are becoming computationally more expensive. One example is the problem of determining how genes within a cell interact with one another. In the inference of gene regulatory networks (GRN) we seek to detect relationships between genes through statistical dependencies in biological data, and as datasets grow, so does computation time. Some algorithms use measures from information theory, which are suitable for detecting nonlinear biological relationships, but incur a high computational cost. We developed InformationMeasures.jl, a package for calculating information theoretic measures. The improvement in performance of our Julia package compared to widely-used packages in other languages enables us to develop new algorithms with higher complexity, examining triples, rather than pairs, of genes. These we can show are more successful than pairwise methods (in simulated data where the underlying GRNs are known), and scale well to the size of the largest currently-available biological datasets.


Speakers
TC

Thalia Chan

Imperial College
Thalia is a Ph.D. student in theoretical systems biology at Imperial College, London. Her research focuses on algorithm development for biological network inference, in particular using information theory. Outside of her studies she contributes to various open source software pro... Read More →


Friday June 23, 2017 2:42pm - 2:54pm
East Pauley Pauley Ballroom, Berkeley, CA

Attendees (35)