文章：

图神经网络在癌症与肿瘤学研究中的应用：新兴趋势与未来展望

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

原文发布日期：15 December 2023

DOI: 10.3390/cancers15245858

类型: Article

开放获取: 是

英文摘要：

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal 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. We then identify the most promising directions for future research. We compare GNNs with graphical models and “non-structured” deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.

摘要翻译： 

下一代癌症与肿瘤学研究需充分利用多模态结构化或图结构信息，其数据类型涵盖从分子结构到空间分辨成像与数字病理学、生物网络及知识图谱。图神经网络（GNNs）能高效整合图结构表征与深度学习的高预测性能，尤其适用于大型多模态数据集。本文综述了2020年至今GNN在癌症与肿瘤学研究中的应用全景，并划分出当前六大主导研究领域。在此基础上，我们进一步明确了未来最具潜力的研究方向。通过将GNN与图模型及"非结构化"深度学习进行对比，本文为癌症与肿瘤学研究者及临床科学家制定了方法学指南，旨在探讨是否应在研究流程中采用GNN方法学。

原文链接：

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends