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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预测神经母细胞瘤患者临床结果的深度神经网络方法。


背景技术来自大型患者群体的高通量组学数据集的可用性使得能够开发旨在预测患者临床结果（例如生存和疾病复发）的方法。这些方法对于更好地了解疾病病因和发展的生物学机制以及治疗反应也很重要。最近，人们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。在这种背景下，深度学习策略因其在广泛的问题和数据集上表现出的卓越性能而受到特别关注。这种策略的主要挑战之一是“小n大p”问题。事实上，与典型的深度学习数据集相比，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含额外的约束来解决这个问题。方法我们建议通过一种新颖的策略来解决这个问题，该策略依赖于基于图的特征提取方法，并结合用于临床结果预测的深度神经网络。组学数据首先表示为图表，其节点代表患者，边代表患者组学概况之间的相关性。然后从每个节点的这些图中提取拓扑特征，例如中心性。最后，这些特征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更准确（DNN：85％-87％，SVM / RF：75％-82％）。 我们探索如何选择不同的参数和配置，以克服小数据问题的影响以及维数灾难。结论我们的结果表明，深度神经网络捕获数据中的复杂特征，有助于预测患者的临床结果。