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Probabilistic graphlets capture biological function in probabilistic molecular networks
Bioinformatics ( IF 5.8 ) Pub Date : 2020-12-29 , DOI: 10.1093/bioinformatics/btaa812
Sergio Doria-Belenguer 1, 2 , Markus K. Youssef 1, 2 , René Böttcher 1 , Noël Malod-Dognin 1, 3 , Nataša Pržulj 1, 3, 4
Affiliation  

Molecular interactions have been successfully modeled and analyzed as networks, where nodes represent molecules and edges represent the interactions between them. These networks revealed that molecules with similar local network structure also have similar biological functions. The most sensitive measures of network structure are based on graphlets. However, graphlet-based methods thus far are only applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that can represent the probability of an interaction occurring in the cell. This information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss.

中文翻译:

概率小图捕获概率分子网络中的生物学功能

分子相互作用已经成功地建模和分析为网络,其中节点代表分子,边缘代表它们之间的相互作用。这些网络表明具有相似局部网络结构的分子也具有相似的生物学功能。网络结构最敏感的度量基于图小图。但是,到目前为止,基于图谱的方法仅适用于未加权的网络,而现实世界中的分子网络可能具有加权的边缘,可以代表细胞中发生相互作用的可能性。在应用阈值生成未加权网络时,通常会丢弃此信息,这可能导致信息丢失。
更新日期:2020-12-31
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