Conference proceeding
M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions
Proceedings (IEEE International Conference on Bioinformatics and Biomedicine), pp 1783-1788
03 Dec 2024
Abstract
Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework's superior performance compared to state-of-the-art models.
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Details
- Title
- M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions
- Creators
- Raeed AL-Sabri - Central South UniversityJianliang Gao - Central South UniversityJiamin Chen - Central South UniversityBabatounde Moctard Oloulade - Central South UniversityZhenpeng Wu - Central South UniversityMonir Abdullah - University of BishaXiaohua Hu - Drexel University
- Publication Details
- Proceedings (IEEE International Conference on Bioinformatics and Biomedicine), pp 1783-1788
- Publisher
- IEEE
- Grant note
- National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Scopus ID
- 2-s2.0-85217276463
- Other Identifier
- 991022018808204721