Detecting functional modules in the yeast protein-protein interaction network

By Jingchun Chen, Bo Yuan. Bioinform., 2006

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Community detection in graphs (2009, positive): “The algorithm finds very good estimates of the modularity maximum, and performs very well on the benchmark of Girvan and Newman (Girvan and Newman, 2002) (Section XV.”
A Comprehensive Survey on Community Detection With Deep Learning (2021, positive): “In metabolic networks [27], [28] and protein-protein interaction (PPI) networks [29], community detection reveals the complexities of metabolisms and identiﬁes proteins with similar biological functions.”
Community detection in social networks (2016, positive): “Chen et al.(22) extended the GN algorithm to partition weighted graphs and used it to identify functional modules in the yeast proteome network.”
Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery (2015, neutral): “…communities allows one to find like-minded people in social networks [1], [2], to improve recommendation systems [3], [4], to segment or classify images [5], [6], to detect protein complexes [7], [8], to find genetically related sub-populations [9], [10], or to discover new tumor subclasses [11].”