{"title":"MODULA:一种基于网络模块的局部蛋白质相互作用网络比对方法","authors":"P. Guzzi, P. Veltri, Swarup Roy, J. Kalita","doi":"10.1109/BIBM.2015.7359918","DOIUrl":null,"url":null,"abstract":"Biological networks are usually used to model interactions among biological macromolecules in a cells. For instance protein-protein interaction networks (PIN) are used to model and analyse the set of interactions among proteins. The comparison of networks may result in the identification of conserved patterns of interactions corresponding to biological relevant entities such as protein complexes and pathways. Several algorithms, known as network alignment algorithms, have been proposed to unravel relations between different species at the interactome level. Algorithms may be categorized in two main classes: merge and mine and mine and merge. Algorithms belonging to the first class initially merge input network into a single integrated and then mine such networks. Conversely algorithms belonging to the second class initially analyze separately two input networks then integrate such results. In this paper we present MODULA (Network Module based PPI Aligner), a novel approach for local network alignment that belong to the second class. The algorithm at first identifies compact modules from input networks. Modules of both networks are then matched using functional knowledge. Then it uses high scoring pairs of modules as seeds to build a bigger alignment. In order to asses MODULA we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset.","PeriodicalId":186217,"journal":{"name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"MODULA: A network module based local protein interaction network alignment method\",\"authors\":\"P. Guzzi, P. Veltri, Swarup Roy, J. Kalita\",\"doi\":\"10.1109/BIBM.2015.7359918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biological networks are usually used to model interactions among biological macromolecules in a cells. For instance protein-protein interaction networks (PIN) are used to model and analyse the set of interactions among proteins. The comparison of networks may result in the identification of conserved patterns of interactions corresponding to biological relevant entities such as protein complexes and pathways. Several algorithms, known as network alignment algorithms, have been proposed to unravel relations between different species at the interactome level. Algorithms may be categorized in two main classes: merge and mine and mine and merge. Algorithms belonging to the first class initially merge input network into a single integrated and then mine such networks. Conversely algorithms belonging to the second class initially analyze separately two input networks then integrate such results. In this paper we present MODULA (Network Module based PPI Aligner), a novel approach for local network alignment that belong to the second class. The algorithm at first identifies compact modules from input networks. Modules of both networks are then matched using functional knowledge. Then it uses high scoring pairs of modules as seeds to build a bigger alignment. In order to asses MODULA we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset.\",\"PeriodicalId\":186217,\"journal\":{\"name\":\"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBM.2015.7359918\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2015.7359918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
生物网络通常用于模拟细胞内生物大分子之间的相互作用。例如,蛋白质-蛋白质相互作用网络(PIN)被用来模拟和分析蛋白质之间的相互作用。网络的比较可能导致识别与生物相关实体(如蛋白质复合物和途径)相对应的相互作用的保守模式。已经提出了几种算法,称为网络对齐算法,以揭示相互作用水平上不同物种之间的关系。算法可以分为两大类:合并和挖掘以及挖掘和合并。第一类算法首先将输入网络合并为单个集成网络,然后对其进行挖掘。相反,第二类算法首先分别分析两个输入网络,然后将这些结果整合。本文提出了基于网络模块的PPI对齐器MODULA (Network Module based PPI Aligner),这是一种用于第二类局部网络对齐的新方法。该算法首先从输入网络中识别紧凑模块。然后使用功能知识对两个网络的模块进行匹配。然后,它使用高分模块对作为种子来构建更大的排列。为了评估MODULA,我们将其与一个相当广泛和更新的数据集上最先进的局部对齐算法进行了比较。
MODULA: A network module based local protein interaction network alignment method
Biological networks are usually used to model interactions among biological macromolecules in a cells. For instance protein-protein interaction networks (PIN) are used to model and analyse the set of interactions among proteins. The comparison of networks may result in the identification of conserved patterns of interactions corresponding to biological relevant entities such as protein complexes and pathways. Several algorithms, known as network alignment algorithms, have been proposed to unravel relations between different species at the interactome level. Algorithms may be categorized in two main classes: merge and mine and mine and merge. Algorithms belonging to the first class initially merge input network into a single integrated and then mine such networks. Conversely algorithms belonging to the second class initially analyze separately two input networks then integrate such results. In this paper we present MODULA (Network Module based PPI Aligner), a novel approach for local network alignment that belong to the second class. The algorithm at first identifies compact modules from input networks. Modules of both networks are then matched using functional knowledge. Then it uses high scoring pairs of modules as seeds to build a bigger alignment. In order to asses MODULA we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset.