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Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins
2021
Entropy
Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE
doi:10.3390/e23101271
pmid:34681995
pmcid:PMC8534328
fatcat:d5krsgqlkfdc3olnkcnm5ksldi