ArticleBMC systems biology2017
CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks.Computational and structural biotechnology journal · 2024Article
- HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes.Genomics, proteomics & bioinformatics · 2023Article
- Condition-Specific Molecular Network Analysis Revealed That Flagellar Proteins Are Involved in Electron Transfer Processes ofGenetics research · 2021Article
- A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations.BMC genomics · 2019Article
- Integrating data and knowledge to identify functional modules of genes: a multilayer approach.BMC bioinformatics · 2019Article
- A bioinformatics potpourri.BMC genomics · 2018Article
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4 authors.
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Abstract
backgroundEffectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.
resultsIn this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained. We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.
conclusionCPredictor3.0 can serve as a promising tool of protein complex prediction.
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