ArticleAlgorithms for molecular biology : AMB2006
A novel functional module detection algorithm for protein-protein interaction networks.
Article in Algorithms for molecular biology : AMB, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 92 citations in OpenAlex.
- Single-Molecule Observation of Competitive Protein-Protein Interactions Utilizing a Nanopore.ACS nano · 2025Article
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- Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.Cell reports. Medicine · 2023Article
- KISL: knowledge-injected semi-supervised learning for biological co-expression network modules.Frontiers in genetics · 2023Article
- Linkage and association analyses reveal that hub genes in energy-flow and lipid biosynthesis pathways form a cluster in upland cotton.Computational and structural biotechnology journal · 2022Article
- Maximizing cohesion and separation for detecting protein functional modules in protein-protein interaction networks.PloS one · 2020Review
- Systematic tracking of altered modules identifies the key biomarkers involved in chronic lymphocytic leukemia.Oncology letters · 2019Article
- An integrated strategy for identifying new targets and inferring the mechanism of action: taking rhein as an example.BMC bioinformatics · 2018Article
- Constructing Networks of Organelle Functional Modules in Arabidopsis.Current genomics · 2016Article
- Computational analysis of protein interaction networks for infectious diseases.Briefings in bioinformatics · 2016Review
- Early gene expression changes in skeletal muscle from SOD1(G93A) amyotrophic lateral sclerosis animal model.Cellular and molecular neurobiology · 2014Article
- Article
- Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study.BMC systems biology · 2013Article
- A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa.PloS one · 2013Article
- The structure of a gene co-expression network reveals biological functions underlying eQTLs.PloS one · 2013Article
- Voting-based cancer module identification by combining topological and data-driven properties.PloS one · 2013Article
- In-silico identification of phenotype-biased functional modules.Proteome science · 2012Article
- Protein complex detection with semi-supervised learning in protein interaction networks.Proteome science · 2011Article
- Integration of breast cancer gene signatures based on graph centrality.BMC systems biology · 2011Article
- BinTree seeking: a novel approach to mine both bi-sparse and cohesive modules in protein interaction networks.PloS one · 2011Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM.
resultsSTM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL) algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches.
conclusionSTM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.