MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity
Zahra Ahmadi, Jamshid Pirgazi, Ali Ghanbari Sorkhi
TL;DR
This paper introduces MoGraphDRP, a deep learning framework that combines multi-omics data and drug features to accurately predict cancer drug responses.
Contribution
The novel contribution is a multi-branch deep learning model with bilinear attention for drug sensitivity prediction using multi-omics and drug graph data.
Findings
MoGraphDRP outperforms existing methods with PCC=0.9689, RMSE=0.6622, and R²=0.9388.
The model effectively distinguishes drug sensitivity and resistance in unknown combinations.
It accurately reconstructs missing IC50 values and integrates diverse data types.
Abstract
Accurate prediction of drug response in cancer cells is a fundamental step toward achieving precision medicine and designing personalized therapies. In this study, a multi-branch deep learning framework is proposed that integrates multi-omics cellular data including gene expression, mutation, methylation, and biological pathways with structural features of drugs (molecular graphs and various chemical fingerprints) to enable drug response prediction. The graph structure of the drug is modeled using a three-layer Graph Convolutional Network (GCN), and chemical fingerprints are compressed using MLP networks. These multiple representations of drugs are integrated and then combined with cellular features in a Multi-head Bilinear Attention module to model the complex interactions between cells and drugs. In the final stage, an ensemble model based on XGBoost is used to refine the outputs. The…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Drug Discovery Methods · Bioinformatics and Genomic Networks · Advanced Graph Neural Networks