Research Group on Systems Pharmacology, Research Unit on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute, University Pompeu Fabra, Parc de Recerca Biomèdica (PRBB), Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain.
Mol Inform. 2019 Jul;38(7):e1900032. doi: 10.1002/minf.201900032. Epub 2019 Apr 8.
With the advent of increasing computational power and large-scale data acquisition, network analysis has become an attractive tool to study the organisation of complex systems and the interrelation of their constituent entities in various scientific domains. In many cases, relations only occur between entities of two different subsets, thereby forming a bipartite network. Often, the analysis of such bipartite networks involves the consideration of its two monopartite projections in order to focus on each entity subset individually as a means to deduce properties of the underlying original network. Although it is broadly acknowledged that this type of projection is not lossless, the inherent limitations of their interpretability are rarely discussed. In this work, we introduce two approaches for measuring the information loss associated with bipartite network projection. Application to two structurally distinct cases in network pharmacology, namely, drug-target and disease-gene bipartite networks, confirms that the major determinant of information loss is the degree of vertices omitted during the monopartite projection.
随着计算能力的提高和大规模数据采集的出现，网络分析已成为研究复杂系统组织和其组成实体在各个科学领域相互关系的一种有吸引力的工具。在许多情况下，关系只发生在两个不同子集的实体之间，从而形成了一个二分网络。通常，分析这种二分网络涉及到考虑其两个单分投影，以便分别关注每个实体子集，作为推断基础原始网络属性的一种手段。尽管人们普遍认识到这种投影不是无损的，但很少讨论其可解释性的固有局限性。在这项工作中，我们引入了两种测量与二分网络投影相关的信息损失的方法。将这两种方法应用于网络药理学中两个结构上不同的情况，即药物-靶标和疾病-基因二分网络，证实信息损失的主要决定因素是单分投影过程中省略的顶点的程度。
