Review
Version 1
Preserved in Portico This version is not peer-reviewed
Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends
Version 1
: Received: 24 October 2023 / Approved: 25 October 2023 / Online: 26 October 2023 (03:33:36 CEST)
A peer-reviewed article of this Preprint also exists.
Gogoshin, G.; Rodin, A.S. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends. Cancers 2023, 15, 5858. Gogoshin, G.; Rodin, A.S. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends. Cancers 2023, 15, 5858.
Abstract
Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.
Keywords
Graph Neural Network; GNN; Deep Learning; Cancer; Oncology; Graphical Model; Bayesian Network
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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