BACKGROUND The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. METHODS We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. RESULTS We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. CONCLUSIONS Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

中文翻译：

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一种深度神经网络方法，可预测神经母细胞瘤患者的临床结局。

背景技术来自大型患者队列的高通量组学数据集的可用性允许开发旨在预测患者临床结果（例如生存率和疾病复发）的方法。这样的方法对于更好地理解疾病病因和发展以及治疗反应的生物学机制也很重要。最近，已经研究了依赖于不同算法（包括支持向量机和随机森林）的不同预测模型。在这种情况下，深度学习策略特别受关注，因为它们在各种问题和数据集上均表现出优异的性能。这种策略的主要挑战之一是“小n大p”问题。的确，相对于典型的深度学习数据集，组学数据集通常由少量样本和大量特征组成。神经网络通常通过特征选择或在学习过程中包括其他约束来解决此问题。方法我们建议使用一种新颖的策略来解决此问题，该策略依赖于基于图的特征提取方法以及用于临床结果预测的深度神经网络。组学数据首先以图表示，其节点代表患者，边缘代表患者的组学概况之间的相关性。然后从这些图中为每个节点提取诸如中心性之类的拓扑特征。最后，这些功能用作训练和测试各种分类器的输入。结果我们将此策略应用于四个神经母细胞瘤数据集，并观察到基于神经网络的模型比最先进的模型（DNN：85％-87％，SVM / RF：75％-82％）更准确。我们探索如何选择不同的参数和配置，以克服小数据问题以及维数诅咒的影响。结论我们的结果表明，深度神经网络捕获了有助于预测患者临床结果的数据中的复杂特征。