Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties between these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, suffer from information loss when modeling physical systems due to their inability to accurately represent multi-object relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. We present a hypergraph-based approach for modeling biological systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs. We then introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of hypergraphlets; i.e., small hypergraphs rooted at a vertex of interest. We empirically evaluate this method on fifteen biological networks and show its potential use in a positive-unlabeled setting to estimate the interactome sizes in various species.

中文翻译：

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具有超图核的生物网络分类。

生物和细胞系统通常被建模为图形，其中顶点代表感兴趣的对象（基因、蛋白质、药物），边代表这些对象之间的关系（绑定到、交互、调节）。由于支持图形分析和学习的理论、方法和软件，这种方法非常成功。然而，由于图无法准确表示多对象关系，因此在对物理系统建模时会丢失信息。超图是图的概括，提供了一个框架来减轻信息丢失并统一不同的基于图的方法。我们提出了一种基于超图的方法来模拟生物系统并制定顶点分类，（超）图上的边缘分类和链接预测问题作为（扩展，双）超图上的顶点分类实例。然后，我们在顶点和边标记（彩色）超图上引入了一种新的核方法，用于分析和学习。该方法基于超图的精确和不精确（通过超图编辑距离）枚举；即，植根于感兴趣的顶点的小超图。我们在 15 个生物网络上凭经验评估了这种方法，并展示了它在正未标记环境中的潜在用途，以估计各种物种的相互作用组大小。植根于感兴趣的顶点的小超图。我们在 15 个生物网络上凭经验评估了这种方法，并展示了它在正未标记环境中的潜在用途，以估计各种物种的相互作用组大小。植根于感兴趣的顶点的小超图。我们在 15 个生物网络上凭经验评估了这种方法，并展示了它在正未标记环境中的潜在用途，以估计各种物种的相互作用组大小。