{"title":"基于PPI网络和蛋白质复合物发现必需蛋白质。","authors":"Jun Ren, Jianxin Wang, Min Li, Fangxiang Wu","doi":"10.1504/ijdmb.2015.068951","DOIUrl":null,"url":null,"abstract":"<p><p>Most computational methods for identifying essential proteins focus on the topological centrality of protein-protein interaction (PPI) networks. However, these methods have limitations, such as the difficulty for identifying essential proteins with low centrality values and the poor performance for incomplete PPI network. In this paper, protein complex is proven to be an important factor for determining protein essentiality and a new centrality measure, complex centrality, is proposed. The weighted average of complex centrality and subgraph centrality, called harmonic centrality (HC), is proposed to predict essential proteins. It combines PPI network topology and protein complex information and has better performance than methods based on PPI network. The improvement is higher when the PPI network is incomplete. Furthermore, a weighted PPI network is generated by integrating cellular localisation and biological process to a PPI network. The performance of HC measure is improved 5% in this weighted PPI network.</p>","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 1","pages":"24-43"},"PeriodicalIF":0.2000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.068951","citationCount":"25","resultStr":"{\"title\":\"Discovering essential proteins based on PPI network and protein complex.\",\"authors\":\"Jun Ren, Jianxin Wang, Min Li, Fangxiang Wu\",\"doi\":\"10.1504/ijdmb.2015.068951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Most computational methods for identifying essential proteins focus on the topological centrality of protein-protein interaction (PPI) networks. However, these methods have limitations, such as the difficulty for identifying essential proteins with low centrality values and the poor performance for incomplete PPI network. In this paper, protein complex is proven to be an important factor for determining protein essentiality and a new centrality measure, complex centrality, is proposed. The weighted average of complex centrality and subgraph centrality, called harmonic centrality (HC), is proposed to predict essential proteins. It combines PPI network topology and protein complex information and has better performance than methods based on PPI network. The improvement is higher when the PPI network is incomplete. Furthermore, a weighted PPI network is generated by integrating cellular localisation and biological process to a PPI network. The performance of HC measure is improved 5% in this weighted PPI network.</p>\",\"PeriodicalId\":54964,\"journal\":{\"name\":\"International Journal of Data Mining and Bioinformatics\",\"volume\":\"12 1\",\"pages\":\"24-43\"},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2015-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1504/ijdmb.2015.068951\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Data Mining and Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1504/ijdmb.2015.068951\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Data Mining and Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1504/ijdmb.2015.068951","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
Discovering essential proteins based on PPI network and protein complex.
Most computational methods for identifying essential proteins focus on the topological centrality of protein-protein interaction (PPI) networks. However, these methods have limitations, such as the difficulty for identifying essential proteins with low centrality values and the poor performance for incomplete PPI network. In this paper, protein complex is proven to be an important factor for determining protein essentiality and a new centrality measure, complex centrality, is proposed. The weighted average of complex centrality and subgraph centrality, called harmonic centrality (HC), is proposed to predict essential proteins. It combines PPI network topology and protein complex information and has better performance than methods based on PPI network. The improvement is higher when the PPI network is incomplete. Furthermore, a weighted PPI network is generated by integrating cellular localisation and biological process to a PPI network. The performance of HC measure is improved 5% in this weighted PPI network.
期刊介绍：
Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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