Mostafavi Sara, Goldenberg Anna, Morris Quaid
Department of Computer Science, Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, ON, Canada.
Methods Mol Biol. 2011;781:399-414. doi: 10.1007/978-1-61779-276-2_20.
A large number of genome-scale networks, including protein-protein and genetic interaction networks, are now available for several organisms. In parallel, many studies have focused on analyzing, characterizing, and modeling these networks. Beyond investigating the topological characteristics such as degree distribution, clustering coefficient, and average shortest-path distance, another area of particular interest is the prediction of nodes (genes) with a given characteristic (labels) - for example prediction of genes that cause a particular phenotype or have a given function. In this chapter, we describe methods and algorithms for predicting node labels from network-based datasets with an emphasis on label propagation algorithms (LPAs) and their relation to local neighborhood methods.
现在已有大量针对多种生物体的基因组规模网络，包括蛋白质-蛋白质相互作用网络和遗传相互作用网络。与此同时，许多研究都聚焦于对这些网络进行分析、特征描述和建模。除了研究诸如度分布、聚类系数和平均最短路径距离等拓扑特征之外，另一个特别受关注的领域是预测具有特定特征（标签）的节点（基因）——例如预测导致特定表型或具有特定功能的基因。在本章中，我们将描述从基于网络的数据集中预测节点标签的方法和算法，重点介绍标签传播算法（LPA）及其与局部邻域方法的关系。
