复杂生物网络分析的图聚类方法研究进展  

Progress of Graph Clustering on Analysis of Complex Biological Networks

作　　者：梅娟[1,2] 王正祥[1,2] 石贵阳[1,2] 李炜疆[1,2] 

机构地区：[1]江南大学生物工程学院,江苏无锡214122 [2]江南大学工业生物技术教育部重点实验室,江苏无锡214122

出　　处：《食品与生物技术学报》2008年第5期15-20,共6页Journal of Food Science and Biotechnology

基　　金：国家863计划项目(2006AA020204)

摘　　要：基因组学和高通量技术提供了大量生命系统组成元件(如蛋白质)之间相互关系的数据,由这些关系数据构成的复杂生物网络蕴含着丰富的生命系统运行机制的知识,挖掘这些隐蔽的知识成为当前系统生物学的主要任务之一。作为知识发现重要手段的图聚类方法,在复杂生物网络分析上受到了普遍关注,在远同源性探测、蛋白质功能预测、代谢途径发现等方面取得了令人瞩目的结果。同时也注意到,由于生命系统的高度复杂性,其他领域中卓有成效的方法往往在复杂生物网络分析中遇到困难。评述了近年来图聚类算法在复杂生物网络分析中的进展,简要分析了复杂生物网络研究的图聚类途径所面临的主要问题。Genomics and high-throughout technologies have produced large amount of relational data about the components of living systems. Such data from various complex networks that carry rich knowledge about the systems. A current challenge of system biology is to mine the hidden knowledge stored in the networks. As an important means for knowledge discovery, graph clustering attracts much attention in analysis of the complex biological networks, attaining remarkable results in remote homology detection, protein function prediction, and metabolic pathway discovery. Meanwhile, due to the high complexity of living systems, methods what successful in other fields often encounter difficulties when applied to the complex biological networks. Here we briefly reviewed the main efforts dedicated to furthering clustering analysis of complex biological networks.

关 键 词：复杂生物网络 图聚类 蛋白质相互作用网络 代谢网络 蛋白质相似性网络 

分 类 号：Q50[生物学—生物化学]