Predicting cryptic links in host-parasite networks.
Predicting cryptic links in host-parasite networks.
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DOI：
10.1371/journal.pcbi.1005557
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发表时间：
2017-05
影响因子：
4.3
通讯作者：
Drake JM
中科院分区：
文献类型：
--
作者：
Dallas T;Park AW;Drake JM
网络是一种表示一个（例如，社交网络）或更多（例如，植物传粉者网络）节点类。预测可能但未观察到的交互的能力已经产生了很大的兴趣，有时被称为链接预测问题。然而，大多数链接预测的研究都集中在社交网络，并假设一个完全普查的网络。在生物网络中，不太可能对所有交互进行普查，忽略交互的不完整检测可能会导致有偏见或不正确的结论。以前预测网络相互作用的尝试依赖于网络结构的已知属性，使得该方法对观察误差敏感。这是一个明显的缺点，因为网络是动态的，有时没有很好的采样，导致不完整的链接检测。在这里，我们开发了一种算法来预测丢失的链接的基础上的条件概率估计和相关的，节点级的功能。我们验证了该算法的模拟数据，然后将其应用到沙漠小型哺乳动物宿主-寄生虫网络。我们的方法在模拟和观测数据上实现了高精度，提供了一种简单的方法来准确预测网络中的缺失链接，而不依赖于有关网络结构的先验知识。大多数宿主-寄生虫关联都知之甚少或根本不知道，因为关联的数量是如此巨大。此外，相互作用可能会改变季节性，或作为主机密度变化的函数。因此，宿主-寄生虫网络的特点可能很差，因为神秘的宿主-寄生虫协会对网络结构的影响是未知的。为了解决这个问题，我们开发了理论并将其应用于经验数据，以测试一个简单算法预测宿主和寄生虫之间相互作用的能力。该算法使用宿主和寄生虫的性状数据来训练宿主-寄生虫相互作用的预测概率模型。我们使用性质差异很大的模拟网络测试了我们方法的准确性，证明了高准确性和鲁棒性。然后，我们将该算法应用于小型哺乳动物宿主-寄生虫网络的数据，估计模型的准确性，确定对预测重要的宿主和寄生虫特征，并量化链接重新标记导致的网络结构特性的预期变化。
Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure. The majority of host-parasite associations are poorly understood or not known at all because the number of associations is so vast. Further, interactions may shift seasonally, or as a function of changing host densities. Consequently, host-parasite networks may be poorly characterized since effects of cryptic host-parasite associations on network structure are unknown. To address this, we developed theory and applied it to empirical data to test the ability of a simple algorithm to predict interactions between hosts and parasites. The algorithm uses host and parasite trait data to train predictive probabilistic models of host-parasite interaction. We tested the accuracy of our approach using simulated networks that vary greatly in their properties, demonstrating high accuracy and robustness. We then applied this algorithm to data on a small mammal host-parasite network, estimated model accuracy, identified host and parasite traits important to prediction, and quantified expected changes to structural properties of the network as a result of link relabeling.