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首页> 外文会议>Pacific Symposium on Biocomputing >AN EFFICIENT ALGORITHM TO INTEGRATE NETWORK AND ATTRIBUTE DATA FOR GENE FUNCTION PREDICTION
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AN EFFICIENT ALGORITHM TO INTEGRATE NETWORK AND ATTRIBUTE DATA FOR GENE FUNCTION PREDICTION

机译：一种基因函数预测集成网络和属性数据的有效算法

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摘要

Label propagation methods are extremely well-suited for a variety of biomedical prediction tasks based on network data. However, these algorithms cannot be used to integrate feature-based data sources with networks. We propose an efficient learning algorithm to integrate these two types of heterogeneous data sources to perform binary prediction tasks on node features (e.g., gene prioritization, disease gene prediction). Our method, LMGraph, consists of two steps. In the first step, we extract a small set of "network features" from the nodes of networks that represent connectivity with labeled nodes in the prediction tasks. In the second step, we apply a simple weighting scheme in conjunction with linear classifiers to combine these network features with other feature data. This two-step procedure allows us to (i) learn highly scalable and computationally efficient linear classifiers, (ii) and seamlessly combine feature-based data sources with networks. Our method is much faster than label propagation which is already known to be computationally efficient on large-scale prediction problems. Experiments on multiple functional interaction networks from three species (mouse, fly, C.elegans) with tens of thousands of nodes and hundreds of binary prediction tasks demonstrate the efficacy of our method.
机译：标签传播方法非常适合基于网络数据的各种生物医学预测任务。但是，这些算法不能用于与网络集成基于功能的数据源。我们提出了一种高效的学习算法来集成这两种类型的异构数据源，以在节点特征（例如，基因优先级，疾病基因预测）上执行二进制预测任务。我们的方法，LMPraph，包括两个步骤。在第一步中，我们从网络的节点中提取一小组“网络功能”，该节点表示与预测任务中标记的节点的连接。在第二步中，我们将简单的加权方案与线性分类器结合使用，以将这些网络功能与其他特征数据组合。这两个步骤允许我们（i）学习高度可扩展和计算的有效的线性分类器，（ii）并与网络无缝地组合基于特征的数据源。我们的方法比标签传播要快得多，这些传播已经已知在大规模预测问题上计算上有效。具有数万个节点的三种（鼠标，飞，C.elegans）的多功能交互网络的实验和数百个二进制预测任务证明了我们方法的功效。

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