RT Journal Article
SR Electronic
T1 Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.11.455993
DO 10.1101/2021.08.11.455993
A1 Hostallero, David Earl
A1 Li, Yihui
A1 Emad, Amin
YR 2022
UL http://biorxiv.org/content/early/2022/04/25/2021.08.11.455993.abstract
AB Motivation The increasing number of publicly available databases containing drugs’ chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy.Results We present two deep learning approaches, BiG-DRP and BiG-DRP+, for drug response prediction. Our models take advantage of the drugs’ chemical structure and the underlying relationships of drugs and cell lines through a bipartite graph and a heterogenous graph convolutional network that incorporate sensitive and resistant cell line information in forming drug representations. Evaluation of our methods and other state-of-the-art models in different scenarios shows that incorporating this bipartite graph significantly improves the prediction performance. Additionally, genes that contribute significantly to the performance of our models also point to important biological processes and signaling pathways. Analysis of predicted drug response of patients’ tumors using our model revealed important associations between mutations and drug sensitivity, illustrating the utility of our model in pharmacogenomics studies.Availability and Implementation An implementation of the algorithms in Python is provided in github.com/ddhostallero/BiG-DRP.Contact amin.emad{at}mcgill.caSupplementary Information Online-only supplementary data is available at the journal’s website.Competing Interest StatementThe authors have declared no competing interest.