| 引用本文: | 代启国,郭茂祖,刘晓燕,王春宇.动态-静态混合的时序蛋白质网络构建方法[J].哈尔滨工业大学学报,2016,48(11):41.DOI:10.11918/j.issn.0367-6234.2016.11.007 |
| DAI Qiguo,GUO Maozu,LIU Xiaoyan,WANG Chunyu.A method of constructing temporal protein networks by hybridizing dynamic and static proteins[J].Journal of Harbin Institute of Technology,2016,48(11):41.DOI:10.11918/j.issn.0367-6234.2016.11.007 |
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| 动态-静态混合的时序蛋白质网络构建方法 |
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代启国1,2,3, 郭茂祖1, 刘晓燕1, 王春宇1
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(1.哈尔滨工业大学 计算机科学与技术学院, 哈尔滨 150001; 2.大连民族大学 计算机科学与工程学院, 辽宁 大连 116600; 3.大连市民族文化数字化重点实验室(大连民族大学), 辽宁 大连116600)
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| 摘要: |
| 目前已公开的蛋白质网络多为静态网络，不能有效描述细胞中蛋白质的动态活动特点.通过融合基因表达数据，研究人员可以构建出描述蛋白质动态性的时序蛋白质网络.现有方法假设所有蛋白质都是动态变化的，而事实上除动态蛋白质外细胞中还包含相对稳定的静态蛋白质.为此，提出了一种基于动态-静态蛋白质混合的时序网络构建新方法.该方法根据基因表达变化情况将蛋白质分为动态和静态两类，并在构建各时刻网络时考虑动态与静态蛋白质之间的相互作用关系.实验结果表明，利用本文方法构建的时序蛋白质网络可以提高蛋白质复合体识别的准确性，从而验证了本文方法的可行性.
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| 关键词: 时序蛋白质网络 蛋白质相互作用 基因表达 生物网络 蛋白质复合体识别 |
| DOI：10.11918/j.issn.0367-6234.2016.11.007 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(2,3,4,2,61271346)；中央高校基本科研业务费专项资金(DC201501030) |
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| A method of constructing temporal protein networks by hybridizing dynamic and static proteins |
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DAI Qiguo1,2,3, GUO Maozu1, LIU Xiaoyan1, WANG Chunyu1
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(1.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China; 2. School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, Liaoning, China; 3.Dalian Key Lab of Digital Technology for National Culture(Dalian Minzu University), Dalian 116600, Liaoning, China)
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| Abstract: |
| Public available protein networks at present are static, which could not be used to describe the dynamic characteristics of proteins in a cell effectively. It is necessary to construct temporal protein network by integrating other biological data, which reflects the dynamic activities of proteins. Most of previous methods assume that all proteins are dynamic. However, in addition to dynamic protein, there are many static proteins in the cell. To this end, this paper proposes a new method to construct a temporal protein network both with dynamic and static proteins. In the method, proteins are classified into two types of dynamic and static, and then a protein network is constructed on each time point by both considering the interactions of dynamic and static proteins. Experimental test results show that the temporal protein network constructed by using the proposed method can improve the accuracy of the identification of protein complexes, which verified the reliability of the proposed method.
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| Key words: temporal protein network protein interaction gene expression biological network protein complex identification |
