图形神经网络在癌症和瘤学研究:新兴和未来的趋势
Grigoriy Gogoshin1, Andrei S Rodin1
1Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA.
Cancers
|December 23, 2023
概括
图形神经网络 (GNN) 正通过整合各种数据类型来彻底改变癌症研究. 本综述指导研究人员采用GNN来获得先进的瘤学见解.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 癌症研究越来越依赖于多式联络,结构化 (图形) 数据,包括分子结构,成像和生物网络.
- 传统的分析方法很难充分利用这些图形数据类型的复杂性和相互联系.
- 深度学习,特别是图形神经网络 (GNN),提供了一种强大的方法来分析图形结构数据.
研究的目的:
- 审查图形神经网络 (GNN) 在癌症和瘤学研究中的近期 (2020年至今) 应用.
- 确定和划分六个主要的研究领域,利用GNN在瘤学.
- 探索有前途的未来研究方向,并为癌症研究管道中的GNN提供采用指南.
主要方法:
- 从2020年到现在,对GNN在癌症和瘤学中的应用进行系统的文献综述.
- 将GNN应用分为六个主要研究领域的分类.
- 对传统图形模型和非结构化深度学习方法进行 GNNs 的比较分析.
主要成果:
- 在癌症研究中确定了六个GNN应用的关键领域,展示了它们的多功能性.
- 在瘤学中常见的大型多模式图数据集上,GNN表现出高的预测性能.
- 该审查强调了GNN在促进癌症和瘤学研究的潜力.
结论:
- 图形神经网络 (GNN) 是下一代癌症和瘤学研究的重要新兴方法.
- 研究人员和医生科学家应该考虑采用GNN来利用多式联络图形数据获得更深入的见解.
- 未来的研究应该专注于扩大GNN应用,并完善其融入临床研究管道.
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