Marinka Zitnik

Fusing bits and DNA

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Graph Convolutional Networks for Computational Pharmacology

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Our paper on graph convolutional networks for modeling polypharmacy side effects has been accepted to ISMB conference. Stay tuned for the final version published in Bioinformatics journal.

We describe a general graph convolutional neural network approach for multirelational link prediction in heterogeneous graphs. In computational pharmacology, this approach creates, for the first time, an opportunity to use large molecular, pharmacological, and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal studies.

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