ArticleBMC systems biology2018
Detecting complexes from edge-weighted PPI networks via genes expression analysis.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods.Frontiers in cell and developmental biology · 2020Article
- Integrating data and knowledge to identify functional modules of genes: a multilayer approach.BMC bioinformatics · 2019Article
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Authors and funding
5 authors.
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Abstract
backgroundIdentifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization.
resultsWe propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks.
conclusionsOn DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S
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