MMGCN:多模多视图卷积网络用于癌症预后预测
Ping Yang1, Wengxiang Chen1, Hang Qiu2
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
Computer methods and programs in biomedicine
|September 13, 2024
概括
本研究引入了一种新的多模式多视图卷积网络 (MMGCN) 框架,通过整合各种患者数据来改善癌症预后. MMGCN有效地捕捉了多种数据类型中的患者相似性,增强了个性化医疗策略.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在瘤学中
背景情况:
- 准确的癌症预后对于个性化医疗和治疗策略制定至关重要.
- 将多模式数据 (遗传,临床) 与深度学习模型集成,可以提高预测准确度.
- 现有的方法往往无法充分利用患者的相似性或有效地捕获各种信息.
研究的目的:
- 提出一个新的框架,多模式多视图卷积网络 (MMGCN),用于增强癌症预后预测.
- 解决现有的多模式深度学习方法在患者相似性和信息整合方面的局限性.
- 提高个性化癌症治疗策略的准确性和有效性.
主要方法:
- 利用相似网络融合 (SNF) 来合并来自基因表达,副本数量改变和临床数据的患者相似网络.
- 开发了多视图图形卷积网络 (GCNs),具有视图级别的注意力机制,以捕捉各种患者的相似性.
- 整合了一个边缘同性恋预测模块,以减轻异性恋边缘对GCN性能的影响.
主要成果:
- 与最先进的方法相比,MMGCN在四个公共癌症数据集 (METABRIC,TCGA-BRCA,TCGA-LGG,TCGA-LUSC) 上表现优越.
- 在接收器运行特征曲线 (AUROC) 下获得的高面积得分,包括METABRIC的0.827 ± 0.005,TCGA-BRCA的0.805 ± 0.014,TCGA-LGG的0.925 ± 0.007,TCGA-LUSC的0.746 ± 0.013.
- 该框架有效地整合了多模式患者信息,以进行可靠的预后预测.
结论:
- 拟议的MMGCN框架通过从不同角度深入探索患者的相似性,显著提高了多模式癌症预后.
- 该研究强调了多视图图形卷积网络在利用复杂的生物数据以改善临床结果方面的潜力.
- MMGCN的源代码是公开的,这有助于进一步的研究和应用在癌症预后.
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