Department of Computer Science, Xiamen University, China.
ITOP Section, DNB Bank ASA, Solheimsgaten, Bergen, Norway.
Brief Bioinform. 2020 Mar 23;21(2):486-497. doi: 10.1093/bib/bbz011.
A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.
生物网络很复杂。一组关键节点决定了这样的网络的质量和状态。越来越多的研究表明，疾病和生物网络是密切相关和相互关联的，某些疾病通常是由生物网络中某些节点的错误引起的。因此，研究生物网络和识别关键节点有助于确定治疗疾病的关键目标。问题是如何高效、低成本地找到网络中的关键节点。现有的识别关键节点的实验方法通常需要大量的时间、人力和资金。因此，许多科学家正试图通过研究高效、低成本的计算方法来解决这个问题。为了便于计算，生物网络通常被建模为几种常见的网络。在这篇综述中，我们根据几种常见计算方法使用的网络类型对生物网络进行分类，并介绍每种网络类型使用的计算方法。
