ArticleBMC bioinformatics2016
A method for predicting protein complex in dynamic PPI networks.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Practical Pipetting Optimization Strategies for Accurate Concentration Measurement of High-Concentration Protein Formulations.AAPS PharmSciTech · 2026Article
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks.Computational and structural biotechnology journal · 2024Article
- A seed expansion-based method to identify essential proteins by integrating protein-protein interaction sub-networks and multiple biological characteristics.BMC bioinformatics · 2023Article
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- Interactome of Arabidopsis Thaliana.Plants (Basel, Switzerland) · 2022Article
- PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.Frontiers in genetics · 2022Article
- An Ensemble Learning Framework for Detecting Protein Complexes From PPI Networks.Frontiers in genetics · 2022Article
- Study on the Potential Molecular Mechanism of Xihuang Pill in the Treatment of Pancreatic Cancer Based on Network Pharmacology and Bioinformatics.Evidence-based complementary and alternative medicine : eCAM · 2022Article
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- Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.Annals of translational medicine · 2020Article
- Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology.Evidence-based complementary and alternative medicine : eCAM · 2020Review
- Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks.BMC bioinformatics · 2019Article
- IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.Nucleic acids research · 2019Article
- Article
- Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.BioMed research international · 2019Article
- CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations.BMC systems biology · 2017Article
- Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.PloS one · 2017Article
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6 authors.
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Abstract
backgroundAccurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.
resultsThe gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.
conclusionsWe construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.
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