# A Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification

arXiv: https://arxiv.org/abs/2302.12838 · HTML: https://mlsanity.com/paper/2302.12838/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification](https://mlsanity.com/paper/1908.06278/) (2019)
- [GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models](https://mlsanity.com/paper/1810.01405/) (2018)
- [Data-Driven Distributionally Robust Electric Vehicle Balancing for Mobility-on-Demand Systems under Demand and Supply Uncertainties](https://mlsanity.com/paper/2210.10887/) (2022)
- [Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization](https://mlsanity.com/paper/2201.04429/) (2022)

## Built on by (most cited first, 10 of 10)

- [LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification](https://mlsanity.com/paper/2408.17384/) (2024)
- [Combining GCN Structural Learning with LLM Chemical Knowledge for Enhanced Virtual Screening](https://mlsanity.com/paper/2504.17497/) (2025)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability](https://mlsanity.com/paper/2406.12897/) (2024)
- [Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification](https://mlsanity.com/paper/2410.05325/) (2024)
- [Introduction to Graph Neural Networks for Machine Learning Engineers](https://mlsanity.com/paper/2412.19419/) (2024)
- [Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs](https://mlsanity.com/paper/2505.11023/) (2025)
- [RGE-GCN: Recursive Gene Elimination with Graph Convolutional Networks for RNA-seq based Early Cancer Detection](https://mlsanity.com/paper/2512.04333/) (2025)
- [MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis](https://mlsanity.com/paper/2512.20026/) (2025)
- [Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models](https://mlsanity.com/paper/2608.20991/) (2026)

## Abstract (arXiv)

The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Current multi-omics integrative models mainly use early fusion by concatenation or late fusion based on deep neural networks. Due to the nature of biological systems, graphs are a better representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods lack testing on a more complex cancer classification task. We propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs that combines both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We test the proposed model on TCGA Pan-cancer dataset and TCGA breast cancer dataset for molecular subtype and cancer subtype classification, respectively. The proposed model outperforms four current state-of-the-art baseline models in multiple evaluation metrics. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information.