RETRACTED ARTICLE: Detangling PPI networks to uncover functionally meaningful clusters

Abstract

BackgroundDecomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called “network modules,” is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO).ResultsWe compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this “detangling” the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters.ConclusionsRe-embedding using the DSD distance metric, before applying standard community detection algorithms, can assist in uncovering GO functionally enriched clusters in the yeast PPI network.

Network Medicine in the Age of Biomedical Big Data

“…Module prediction and identifying non-overlapping clusters with the PPI remains challenging since the PPI network has a short diameter, i.e., most nodes are close to all other nodes in terms of network distance. Novel distance metrics and community detection algorithms have been proposed to overcome this problem (Hall-Swan et al, 2018). The recently proposed DIseAse MOdule Detection (DIAMOnD) algorithm (Ghiassian et al, 2015) associates the functional modules of known disease-associated proteins (seed proteins) and identifies the close neighbors of these genes (candidate disease-associated proteins) using topological properties of the interactome.…”

Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC)

“…After the workshop, 12 papers [1][2][3][4][5][6][7][8][9][10][11][12] have been accepted for publication in the CNB-MAC 2017 partner journals after an additional round of review and revision. The following journals have partnered with CNB-MAC 2017: BMC Bioinformatics, BMC Genomics, BMC Systems Biology, and IET Systems Biology.…”

“…In [9], Hall-Swan et al compares popular network clustering methods for decomposing a protein-protein interaction (PPI) network into non-overlapping network modules. Clustering PPI networks provides an efficient means of analyzing the organization of PPI networks and may be used to detect novel functional modules that are embedded in the network.…”

Retraction Note: detangling PPI networks to uncover functionally meaningful clusters

“…The authors have retracted this article [1]. After publication they discovered a technical error in the Louvain algorithm with bounded cluster sizes.…”