BACKGROUND One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. RESULTS We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. CONCLUSION We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. REVIEWERS This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

中文翻译：

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使用基于网络的综合方法预测神经母细胞瘤患者的临床结果。

背景技术计算生物学中当前的主要挑战之一是弄清正在产生的大量多维实验数据。例如，经常使用不同的高通量技术对大量患者进行筛查，从而有效地为数百或数千患者提供多种患者特异性分子概况。结果我们提出并实现了一种基于网络的方法，该方法将此类患者组学数据集成到患者相似性网络中。然后，将从这些网络派生的拓扑特征用于预测相关的临床特征。作为2017年CAMDA挑战的一部分，我们已成功地将此策略应用于神经母细胞瘤数据集，该数据集由基因组和转录组数据组成。特别是，我们观察到，基于我们基于网络的方法构建的模型至少具有与现有模型一样的性能。此外，我们还探索了各种拓扑功能的有效性，并观察到，例如，冗余的中心度度量可以组合以构建更强大的模型。结论我们证明了从组学数据推断出的网络包含临床相关信息，并且仅使用网络拓扑数据就可以预测患者的临床结果。审阅者本文由Liu-Yu Liu，Tomislav Smuc和Isabel Nepomuceno审阅。结论我们证明了从组学数据推断出的网络包含临床相关信息，并且仅使用网络拓扑数据就可以预测患者的临床结果。审阅者本文由Liu-Yu Liu，Tomislav Smuc和Isabel Nepomuceno审阅。结论我们证明了从组学数据推断出的网络包含临床相关信息，并且仅使用网络拓扑数据就可以预测患者的临床结果。审阅者本文由Liu-Yu Liu，Tomislav Smuc和Isabel Nepomuceno审阅。