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Evaluating predictive performance of network biomarkers with network structures

机译：用网络结构评估网络生物标志物的预测性能

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摘要

Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts.
机译：网络是一个强大的结构，可揭示基础数据的宝贵特征。但是，先前评估基于网络的生物标记物的预测性能的工作并未考虑节点连接性。我们认为有必要通过采用适当的技术来最大化网络结构的收益。为了解决这个问题，我们的目标是从定量表达（例如基因表达数据）中了解网络中每个节点的权重系数。权重系数是根据优化问题计算得出的，该问题使网络结构中节点之间的总加权差异最小。这可以用图拉普拉斯算子表示。在获得网络标记的系数向量之后，我们可以计算相应的网络预测器。我们通过使用公开的乳腺癌生物标志物与三个患者队列进行实验，证明了所提出方法的有效性。首先根据与癌症标志相关的GO术语对网络标志进行分组。我们比较跨基因表达数据集的每个网络标记组的预测性能。我们还针对特征聚合的平均方法评估了网络预测变量。报告的结果表明，网络标记的预测性能通常在不同患者队列中不一致。

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