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Networks - learning salient gene and protein features from network topologies.

Banff International Research Station for Mathematical Innovation and Discovery

Duncan Forster is PhD student in Molecular Genetics co-supervised by Prof Gary Bader and Charlie Boone at the University of Toronto. https://baderlab.org/Members His work has addressed the following questions. Firstly, we wanted to determine whether recent deep learning architectures (namely graph neural networks/graph convolutional networks) could be used to learn salient gene and protein features from network topologies. If so, these features could be integrated in a trainable, end-to-end fashion allowing for effective integration of biological networks. These recent deep learning architectures have shown substantial improvements over previous network feature learning approaches on a range of tasks, which motivates their use in biological domains. Secondly, we wanted to determine more effective evaluation strategies in order to compare integration approaches. This is a challenging task due to differences in input network sizes and standard coverage, biases and quality of the standards, differences in method outputs (networks vs. features), and biases in the current evaluation strategies themselves. Code is available at https://github.com/bowang-lab/BIONIC

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University of Toronto

2020-12-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76782

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/