期刊文献+

Mining Protein Complexes from PPI Networks Using the Minimum Vertex Cut 被引量:1

Mining Protein Complexes from PPI Networks Using the Minimum Vertex Cut
原文传递
导出
摘要 Evidence shows that biological systems are composed of separable functional modules. Identifying protein complexes is essential for understanding the principles of cellular functions. Many methods have been proposed to mine protein complexes from protein-protein interaction networks. However, the performances of these algorithms are not good enough since the protein-protein interactions detected from experiments are not complete and have noise. This paper presents an analysis of the topological properties of protein complexes to show that although proteins from the same complex are more highly connected than proteins from different complexes, many protein complexes are not very dense (density ≥0.8). A method is then given to mine protein complexes that are relatively dense (density ≥0.4). In the first step, a topology property is used to identify proteins that are probably in a same complex. Then, a possible boundary is calculated based on a minimum vertex cut for the protein complex. The final complex is formed by the proteins within the boundary. The method is validated on a yeast protein-protein interaction network. The results show that this method has better performance in terms of sensitivity and specificity compared with other methods. The functional consistency is also good. Evidence shows that biological systems are composed of separable functional modules. Identifying protein complexes is essential for understanding the principles of cellular functions. Many methods have been proposed to mine protein complexes from protein-protein interaction networks. However, the performances of these algorithms are not good enough since the protein-protein interactions detected from experiments are not complete and have noise. This paper presents an analysis of the topological properties of protein complexes to show that although proteins from the same complex are more highly connected than proteins from different complexes, many protein complexes are not very dense (density ≥0.8). A method is then given to mine protein complexes that are relatively dense (density ≥0.4). In the first step, a topology property is used to identify proteins that are probably in a same complex. Then, a possible boundary is calculated based on a minimum vertex cut for the protein complex. The final complex is formed by the proteins within the boundary. The method is validated on a yeast protein-protein interaction network. The results show that this method has better performance in terms of sensitivity and specificity compared with other methods. The functional consistency is also good.
出处 《Tsinghua Science and Technology》 SCIE EI CAS 2012年第6期674-681,共8页 清华大学学报(自然科学版(英文版)
基金 Supported in part by the National Natural Science Foundation of China (Nos.61232001 and 61073036)
关键词 protein complex protein-protein interaction network minimum vertex cut protein complex protein-protein interaction network minimum vertex cut
  • 相关文献

参考文献29

  • 1Kumar A, Snyder M. Protein complexes take the bait. Nature, 2002, 415(6868): 123-124.
  • 2Li M, Wang J X, Chen J E. A fast agglomerate algorithm for mining functional modules in protein interaction networks. In: Proceedings of International Conference on BioMedical Engineering and Informatics. Sanya, China, 2008.
  • 3Chua H N, Ning K, Sung W K, Leong H W, Wong L. Using indirect protein-protein interactions for protein complex prediction. J. Bioinform. Comput. Biol., 2008, 6(3): 435- 466.
  • 4Spirin V, Mirny L A. Protein complexes and functional modules in molecular networks. In: Proceedings of the National Academy of Sciences of Unitied States of America. The National Academy of Sciences, USA, 2003.
  • 5Pei P, Zhang A. A seed-refine algorithm for detecting protein complexes from protein interaction data. IEEE Transactions on Nanobioscience, 2007, 6(1): 43-50.
  • 6Afnizanfaizal A, Safaai D, Siti Z M H, Hamimah M. Graph partitioning method for functional module detections of protein interaction network. In: International Conference on Computer Technology and Development. Washington D.C., USA, 2009.
  • 7Bader G, Hogue C. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(2): 1-27.
  • 8Zhang S, Ning X, Liu H, Zhang X. Prediction of protein complexes based on protein interaction data and functional annotation data using kernel methods. In: International Conference on Intelligent Computing. Springer-Verlag Berlin, Heidelberg, 2006.
  • 9Adamcsek B. CFinder: Locating cliques and overlapping modules in biological networks. Bioinformatics, 2006, 22(8): 1021-1023.
  • 10Li X, Tan S, Foo C, Ng S. Interaction graph mining for protein complexes using local clique merging. Genome Informatics, 2005, 16(2): 260-269.

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部