MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Background and objective

Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating information from various modalities, such as genetic and clinical data, and developing multi-modal deep learning models can enhance prediction accuracy. However, most existing multi-modal deep learning methods either overlook patient similarities that benefit prognosis prediction or fail to effectively capture diverse information due to measuring patient similarities from a single perspective. To address these issues, a novel framework called multi-modal multi-view graph convolutional networks (MMGCN) is proposed for cancer prognosis prediction.

Methods

Initially, we utilize the similarity network fusion (SNF) algorithm to merge patient similarity networks (PSNs), individually constructed using gene expression, copy number alteration, and clinical data, into a fused PSN for integrating multi-modal information. To capture diverse perspectives of patient similarities, we treat the fused PSN as a multi-view graph by considering each single-edge-type subgraph as a view graph, and propose multi-view graph convolutional networks (GCNs) with a view-level attention mechanism. Moreover, an edge homophily prediction module is designed to alleviate the adverse effects of heterophilic edges on the representation power of GCNs. Finally, comprehensive representations of patient nodes are obtained to predict cancer prognosis.

Results

Experimental results demonstrate that MMGCN outperforms state-of-the-art baselines on four public datasets, including METABRIC, TCGA-BRCA, TCGA-LGG, and TCGA-LUSC, with the area under the receiver operating characteristic curve achieving 0.827 ± 0.005, 0.805 ± 0.014, 0.925 ± 0.007, and 0.746 ± 0.013, respectively.

Conclusions

Our study reveals the effectiveness of the proposed MMGCN, which deeply explores patient similarities related to different modalities from a broad perspective, in enhancing the performance of multi-modal cancer prognosis prediction. The source code is publicly available at https://github.com/ping-y/MMGCN.

MMGCN:用于癌症预后预测的多模态多视图卷积网络
背景和目的对癌症患者进行准确的预后预测在制定治疗策略方面发挥着重要作用,对个性化医疗产生了重大影响。该领域的最新进展表明,整合遗传和临床数据等各种模式的信息并开发多模式深度学习模型可以提高预测的准确性。然而,大多数现有的多模态深度学习方法要么忽略了有利于预后预测的患者相似性,要么由于从单一角度测量患者相似性而无法有效捕捉各种信息。为了解决这些问题,我们提出了一种用于癌症预后预测的新型框架,称为多模态多视角图卷积网络(MMGCN)。方法最初,我们利用相似性网络融合(SNF)算法,将利用基因表达、拷贝数改变和临床数据单独构建的患者相似性网络(PSN)合并为一个融合的PSN,以整合多模态信息。为了从不同角度捕捉患者的相似性,我们将融合后的患者相似性网络视为多视图图,将每个单边型子图视为视图图,并提出了具有视图级关注机制的多视图卷积网络(GCN)。此外,还设计了边缘同亲预测模块,以减轻异亲边缘对 GCN 表示力的不利影响。结果实验结果表明,在 METABRIC、TCGA-BRCA、TCGA-LGG 和 TCGA-LUSC 等四个公共数据集上,MMGCN 的表现优于最先进的基线,接收者操作特征曲线下面积分别为 0.结论我们的研究揭示了所提出的 MMGCN 在提高多模态癌症预后预测性能方面的有效性,它从广阔的视角深入探讨了与不同模态相关的患者相似性。源代码可在 https://github.com/ping-y/MMGCN 公开获取。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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