# AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

arXiv: https://arxiv.org/abs/2112.08366 · HTML: https://mlsanity.com/paper/2112.08366/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response](https://mlsanity.com/paper/2005.09572/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep learning methods for drug response prediction in cancer: predominant and emerging trends](https://mlsanity.com/paper/2211.10442/) (2022)
- [TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs](https://mlsanity.com/paper/2411.03464/) (2024)
- [Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks](https://mlsanity.com/paper/2509.11493/) (2025)

## Abstract (arXiv)

Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph (MeG) for each cell line, and then aggregates multi-omics features to predict drug response using a novel structure, called Graph edge-aware Network (GeNet). For the first time, our AGMI approach explores gene constraint based multi-omics integration for DRP with the whole-genome using GNNs. Empirical experiments on the CCLE and GDSC datasets show that our AGMI largely outperforms state-of-the-art DRP methods by 8.3%--34.2% on four metrics. Our data and code are available at https://github.com/yivan-WYYGDSG/AGMI.