/EVANS_GNNCDR_ISMB2022

Info and References page for Nathaniel Evans poster presentation at ISMB 2022, titled: "Prediction of drug-perturbed cancer cell line gene expression using graph neural networks"

Prediction of drug-perturbed cancer cell line gene expression using graph neural networks

Author, primary contact: Nathaniel Evans, evansna@ohsu.edu
Co-Authors: *Guanming Wu, Ph.D. , *Xubo Song, Ph.D., † Gordon Mills, Ph.D., M.D., *Shannon McWeeney, Ph.D.

*Division of Bioinformatics & Computational Biology, Department of  Medical Informatics & Clinical Epidemiology,  Oregon Health & Science University
† Division of Developmental and Cancer Biology, Oregon Health & Science University

Ineffective or limited precision oncology treatments are a cause of patient mortality. We seek to address this challenge by improving pre-clinical drug repurposing and drug combination discovery. We highlight the methodological challenge of training drug response models using single-drug data that will generalize well to multi-drug perturbations. We operate on the premise that protein-protein interactions mediate cellular drug response and hypothesize that incorporating this prior knowledge in a deep learning framework is liable to overcome limitations in drug response modeling. To do this we propose a machine learning model to predict drug-perturbed mRNA expression from intrinsic cancer features using graph neural networks (GNN) that operate on literature curated protein functional-interactions and drug-target interactions. We have shown promise of our approach using synthetic data and are in-progress of applying it to experimental datasets (LINCS L1000). The successful outcome of our method will enable novel GNN-based approaches to drug prioritization.

Research reported in this poster was supported by National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR002371.

References

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