[1]刘光明,杨柳,高盼盼,等.融合蛋白质复合体的人类蛋白互作网络功能模块发现[J].智能系统学报,2016,11(5):703-710.[doi:10.11992/tis.201603034]
LIU Guangming,YANG Liu,GAO Panpan,et al.The functional module detection of PPI network by incorporating protein complex data[J].CAAI Transactions on Intelligent Systems,2016,11(5):703-710.[doi:10.11992/tis.201603034]
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LIU Guangming,YANG Liu,GAO Panpan,et al.The functional module detection of PPI network by incorporating protein complex data[J].CAAI Transactions on Intelligent Systems,2016,11(5):703-710.[doi:10.11992/tis.201603034]
融合蛋白质复合体的人类蛋白互作网络功能模块发现
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第5期
页码:
703-710
栏目:
学术论文—脑认知基础
出版日期:
2016-11-01
- Title:
- The functional module detection of PPI network by incorporating protein complex data
- Keywords:
- PPI; protein complex; functional module; module detection; gene ontology; pathway
- 分类号:
- TP391
- 摘要:
- 人类蛋白互作网络中功能模块的检测是目前网络医学研究的一个热点问题。好的功能模块可以帮助我们更好地去理解和认识蛋白质相互作用的分子机理。近年来的一些研究大多数是基于复杂网络中的拓扑模块发现算法对蛋白质相互作用网络进行模块划分，然后对其进行生物学上的功能研究。由于PPI网络中的蛋白之间相互作用的数据获取的不完整，相关研究表明目前人类只获得了人类蛋白之间相互作用数据的10%~20%，其中已经获取的数据中还包含着一些噪声，这就导致基于拓扑结构的社团检测算法的精度降低。为了克服这个问题，本文将蛋白质复合体数据融入到模块检测算法中，分别使用K-Means和NMF算法对PPI网络进行模块划分，然后从基因本体和通路2个方面对检测到的模块进行功能分析。实验结果表明融合了蛋白质复合体的PPI网络更容易得到具有生物学意义的功能模块。
- Abstract:
- Functional module detection of protein-protein interaction (PPI) network has been a major challenge identified recently by medical researchers. It allows understanding and recognizing the interaction between proteins in an efficient manner. In this study, topological module detection methods, popular in the field of complex protein networks, were applied to the PPI network to obtain these modules, followed by a biological analysis of the topological modules. The interaction mechanism was observed for only 10%~20% of the protein pairs because of incomplete PPI data. Furthermore, the data for noise interaction always existed in PPI; therefore, the number of biologically precise modules decreased according to topological community-detection methods. In this study, the protein complex data was incorporated into the PPI network to identify more biologically precise protein modules. K-Means clustering and non-negative matrix factorization algorithms were used to segregate the PPI network into different modules. Gene ontology (GO) and pathway analysis were conducted for each of these modules to quantify their biological significance. The results of the experiments showed that the modules detected by combining the protein complex and PPI network demonstrate a higher tendency to achieve larger homogeneity values compared with those detected using GO and pathway analysis.
- 参考文献/References:
-
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- 相似文献/References:
-
[1]刘晓,陈璟,王子祥.用于成对PPI网络比对的分治与整合算法[J].智能系统学报,2022,17(5):960.[doi:10.11992/tis.202106001]
LIU Xiao,CHEN Jing,WANG Zixiang.A divide-and-conquer and integration algorithm for pairwise alignment of PPI networks[J].CAAI Transactions on Intelligent Systems,2022,17():960.[doi:10.11992/tis.202106001]
备注/Memo
收稿日期:2016-03-18。
基金项目:国家自然科学基金项目（61105055，81230086）.
作者简介:刘光明,男,1986年生,博士研究生,主要研究方向为复杂网络、数据挖掘、蛋白质功能模块;杨柳,女,1980年生,博士研究生,主要研究方向为机器学习、数据挖掘。高盼盼,女,1989年生,硕士研究生,主要研究方向为基于药物副作用的分子机理的研究、数据挖掘。
通讯作者:刘光明.E-mail:guangmingliu@bjtu.edu.cn
更新日期/Last Update:
1900-01-01