Brief Bioinform. 2017 Nov 1;18(6):1057-1070. doi: 10.1093/bib/bbw071.
The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.
遗传、蛋白质组学、疾病和药理学研究产生了丰富的蛋白质相互作用、疾病调控和药物活性数据,这些数据可用于对生物、疾病和药物治疗过程进行系统水平的研究。这些研究得益于已建立和新兴的计算方法。最近,其他学科开发的网络描述符也越来越多地用于研究蛋白质-蛋白质、基因调控、代谢、疾病网络。在公共网络服务器中,这些有用的网络特征的覆盖范围不足。因此,我们在 PROFEAT 网络服务器中引入了 313 种文献报道的网络描述符,用于描述无向无权重(均匀结合常数和分子水平)、无向边权重(变化的结合常数)、无向节点权重(变化的分子水平)、无向边-节点权重(变化的结合常数和分子水平)和有向无权重(有向过程)网络的拓扑、连通性和复杂性特征。PROFEAT 计算的网络描述符的有用性通过它们在研究蛋白质-蛋白质、基因调控、基因共表达、蛋白质-药物和代谢网络中的文献报道应用得到了说明。PROFEAT 可在 http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi 上免费访问。