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• W2799351678 abstract "Identifying 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. We 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. On 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 n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria." @default.
• W2799351678 created "2018-05-17" @default.
• W2799351678 creator A5001619694 @default.
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• W2799351678 date "2018-04-01" @default.
• W2799351678 modified "2023-09-26" @default.
• W2799351678 title "Detecting complexes from edge-weighted PPI networks via genes expression analysis" @default.
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• W2799351678 doi "https://doi.org/10.1186/s12918-018-0565-y" @default.
• W2799351678 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5998908" @default.
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