ArticleHuman genomics2016
Identification of protein complexes from multi-relationship protein interaction networks.
Article in Human genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Integration of Gastric Cancer RNA-Seq Datasets Along With PPI Network Suggests That Nonhub Nodes Have the Potential to Become Biomarkers.Cancer reports (Hoboken, N.J.) · 2025Article
- A novel method to predict essential proteins based on tensor and HITS algorithm.Human genomics · 2020Article
- Analyzing the genes and pathways related to major depressive disorder via a systems biology approach.Brain and behavior · 2020Article
- A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks.Frontiers in genetics · 2020Article
- Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks.BMC bioinformatics · 2018Article
- CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.International journal of molecular sciences · 2017Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.
resultsThere exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.
conclusionsThe experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.