Exploiting locational and topological overlap model to identify modules in protein interaction networks
Publication in refereed journal

替代計量分析
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其它資訊
摘要Background
Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.

Results
In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL.

Conclusion
Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.
著者Lixin Cheng, Pengfei Liu, Dong Wang, Kwong-Sak Leung
期刊名稱BMC Bioinformatics
出版年份2019
月份1
日期14
卷號20
出版社BioMed Central
文章號碼23
國際標準期刊號1471-2105
語言英式英語
關鍵詞Protein interaction network, Network clustering, Subcellular localization, Topological overlap, Functional module

上次更新時間 2020-23-10 於 00:09