AUTHOR=Gervits Asia , Sharan Roded 

TITLE=Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder

JOURNAL=Frontiers in Bioinformatics

VOLUME=2

YEAR=2022

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2022.1025783

DOI=10.3389/fbinf.2022.1025783

ISSN=2673-7647

ABSTRACT=<p>Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: <ext-link ext-link-type="uri" xlink:href="https://github.com/aijag/drugGraphNet" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aijag/drugGraphNet</ext-link>.</p>