由于表现和症状的广泛性，人类疾病的异质性给诊断和治疗带来了挑战。随着标记多组学数据的快速发展，综合机器学习方法通​​过在更细粒度的水平上重新定义这些疾病，在治疗方面取得了突破。这些方法通常在可扩展性、过度简化和丢失数据的处理方面存在局限性。在本研究中，我们引入了多组学图诊断（MOGDx），这是一种灵活的命令行工具，用于集成多组学数据以执行异构分类任务疾病。 MOGDx 有一个网络分类法。它融合了患者相似性网络，通过基因组数据的简化向量表示增强了该集成网络，并使用图卷积网络执行分类。 MOGDx 在癌症基因组图谱中的三个数据集上进行了评估，其中涉及乳腺癌、肾癌和低级别胶质瘤。 MOGDx 展示了最先进的性能以及在每项任务中识别相关多组学标记的能力。与其他网络整合方法相比，它整合了更多的基因组测量和更大的患者覆盖范围。总体而言，MOGDx 是一种很有前景的工具，可用于整合多组学数据、对异质疾病进行分类以及帮助解释基因组标记数据。MOGDx 源代码可从 https://github.com/biomedicalinformaticsgroup/MOGDx https://github.com/ 获取biomedicalinformaticsgroup/MOGDx。补充材料可在随附文件 SupplementaryMaterial.pdf 中找到。© 作者 2024。由牛津大学出版社出版。

Heterogeneity in human diseases presents challenges in diagnosis and treatments due to the broad range of manifestations and symptoms. With the rapid development of labelled multi-omic data, integrative machine learning methods have achieved breakthroughs in treatments by redefining these diseases at a more granular level. These approaches often have limitations in scalability, oversimplification, and handling of missing data.In this study, we introduce Multi-Omic Graph Diagnosis (MOGDx), a flexible command line tool for the integration of multi-omic data to perform classification tasks for heterogeneous diseases. MOGDx has a network taxonomy. It fuses patient similarity networks, augments this integrated network with a reduced vector representation of genomic data and performs classification using a graph convolutional network. MOGDx was evaluated on three datasets from the cancer genome atlas for breast invasive carcinoma, kidney cancer, and low grade glioma. MOGDx demonstrated state-of-the-art performance and an ability to identify relevant multi-omic markers in each task. It integrated more genomic measures with greater patient coverage compared to other network integrative methods. Overall, MOGDx is a promising tool for integrating multi-omic data, classifying heterogeneous diseases, and aiding interpretation of genomic marker data.MOGDx source code is available from https://github.com/biomedicalinformaticsgroup/MOGDxhttps://github.com/biomedicalinformaticsgroup/MOGDx.Supplementary material is available in the accompanying file SupplementaryMaterial.pdf.© The Author(s) 2024. Published by Oxford University Press.