# Multi-modal Graph Learning for Disease Prediction

arXiv: https://arxiv.org/abs/2203.05880 · HTML: https://mlsanity.com/paper/2203.05880/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [Learning Discrete Structures for Graph Neural Networks](https://mlsanity.com/paper/1903.11960/) (2019)
- [Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings](https://mlsanity.com/paper/2006.13009/) (2020)
- [How to learn a graph from smooth signals](https://mlsanity.com/paper/1601.02513/) (2016)
- [Spectral Graph Convolutions for Population-based Disease Prediction](https://mlsanity.com/paper/1703.03020/) (2017)
- [Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification](https://mlsanity.com/paper/1711.05859/) (2017)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [Latent-Graph Learning for Disease Prediction](https://mlsanity.com/paper/2003.13620/) (2020)
- [Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example](https://mlsanity.com/paper/1611.06066/) (2016)
- [Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction](https://mlsanity.com/paper/2009.02759/) (2020)
- [Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning](https://mlsanity.com/paper/2005.03227/) (2020)
- [A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning](https://mlsanity.com/paper/1802.06260/) (2018)
- [Self-Attention Equipped Graph Convolutions for Disease Prediction](https://mlsanity.com/paper/1812.09954/) (2018)
- [Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification](https://mlsanity.com/paper/1310.0890/) (2013)
- [Multi-hop Convolutions on Weighted Graphs](https://mlsanity.com/paper/1911.04978/) (2019)

## Built on by (most cited first, 30 of 34)

- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Node-oriented Spectral Filtering for Graph Neural Networks](https://mlsanity.com/paper/2212.03654/) (2022)
- [Dynamic Multimodal Information Bottleneck for Multimodality Classification](https://mlsanity.com/paper/2311.01066/) (2023)
- [RDGSL: Dynamic Graph Representation Learning with Structure Learning](https://mlsanity.com/paper/2309.02025/) (2023)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Graph Neural Networks in Histopathology: Emerging Trends and Future Directions](https://mlsanity.com/paper/2406.12808/) (2024)
- [Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications](https://mlsanity.com/paper/2411.03782/) (2024)
- [MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction](https://mlsanity.com/paper/2406.14455/) (2024)
- [Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach](https://mlsanity.com/paper/2602.04116/) (2026)
- [Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications](https://mlsanity.com/paper/2402.12608/) (2024)
- [FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction](https://mlsanity.com/paper/2406.11928/) (2024)
- [Completed Feature Disentanglement Learning for Multimodal MRIs Analysis](https://mlsanity.com/paper/2407.04916/) (2024)
- [Multimodal brain age estimation using interpretable adaptive population-graph learning](https://mlsanity.com/paper/2307.04639/) (2023)
- [MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction](https://mlsanity.com/paper/2307.07093/) (2023)
- [Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume](https://mlsanity.com/paper/2208.03028/) (2022)
- [Evidence-aware multi-modal data fusion and its application to total knee replacement prediction](https://mlsanity.com/paper/2303.13810/) (2023)
- [LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees](https://mlsanity.com/paper/2305.01379/) (2023)
- [Multi-Dimension-Embedding-Aware Modality Fusion Transformer for Psychiatric Disorder Clasification](https://mlsanity.com/paper/2310.02690/) (2023)
- [AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine](https://mlsanity.com/paper/2311.14304/) (2023)
- [A Graph-Based Framework for Interpretable Whole Slide Image Analysis](https://mlsanity.com/paper/2503.11846/) (2025)
- [Fine-Grained Traceability for Transparent ML Pipelines](https://mlsanity.com/paper/2601.14971/) (2026)
- [Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning](https://mlsanity.com/paper/2602.00067/) (2026)
- [CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data](https://mlsanity.com/paper/2603.20016/) (2026)
- [Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving](https://mlsanity.com/paper/2302.00986/) (2023)
- [Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery](https://mlsanity.com/paper/2303.03388/) (2023)
- [Unleashing the potential of GNNs via Bi-directional Knowledge Transfer](https://mlsanity.com/paper/2310.17132/) (2023)
- [Classification of developmental and brain disorders via graph convolutional aggregation](https://mlsanity.com/paper/2311.07370/) (2023)
- [Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving](https://mlsanity.com/paper/2409.00839/) (2024)
- [Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning](https://mlsanity.com/paper/2410.17494/) (2024)
- [A Brain-to-Population Graph Learning Framework for Diagnosing Brain Disorders](https://mlsanity.com/paper/2506.16096/) (2025)

## Abstract (arXiv)

Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e.g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL). However, constructing an appropriate graph in advance is not a simple matter for these methods. Meanwhile, the complex correlation between modalities is ignored. These factors inevitably yield the inadequacy of providing sufficient information about the patient's condition for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. To effectively exploit the rich information across multi-modality associated with the disease, modality-aware representation learning is proposed to aggregate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the graph manually, the latent graph structure is captured through an effective way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction tasks demonstrates that the proposed MMGL achieves more favorable performance. The code of MMGL is available at urlhttps://github.com/SsGood/MMGL.