ArticleBioMed research international2014
A novel algorithm for detecting protein complexes with the breadth first search.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 13 citations in OpenAlex.
- Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes.Scientific reports · 2020Article
- Article
- Detecting conserved protein complexes using a dividing-and-matching algorithm and unequally lenient criteria for network comparison.Algorithms for molecular biology : AMB · 2015Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Most biological processes are carried out by protein complexes. A substantial number of false positives of the protein-protein interaction (PPI) data can compromise the utility of the datasets for complexes reconstruction. In order to reduce the impact of such discrepancies, a number of data integration and affinity scoring schemes have been devised. The methods encode the reliabilities (confidence) of physical interactions between pairs of proteins. The challenge now is to identify novel and meaningful protein complexes from the weighted PPI network. To address this problem, a novel protein complex mining algorithm ClusterBFS (Cluster with Breadth-First Search) is proposed. Based on the weighted density, ClusterBFS detects protein complexes of the weighted network by the breadth first search algorithm, which originates from a given seed protein used as starting-point. The experimental results show that ClusterBFS performs significantly better than the other computational approaches in terms of the identification of protein complexes.
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