ArticleBMC bioinformatics2007
Semantic integration to identify overlapping functional modules in protein interaction networks.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
- Protein complexes detection based on node local properties and gene expression in PPI weighted networks.BMC bioinformatics · 2022Article
- Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward.Computational and structural biotechnology journal · 2022Review
- Approximate search for known gene clusters in new genomes using PQ-trees.Algorithms for molecular biology : AMB · 2021Article
- Application of the Interaction between Tissue Immunohistochemistry Staining and Clinicopathological Factors for Evaluating the Risk of Oral Cancer Progression by Hierarchical Clustering Analysis: A Case-Control Study in a Taiwanese Population.Diagnostics (Basel, Switzerland) · 2021Article
- Efficient and accurate identification of protein complexes from protein-protein interaction networks based on the clustering coefficient.Computational and structural biotechnology journal · 2021Article
- A New Method for Recognizing Protein Complexes Based on Protein Interaction Networks and GO Terms.Frontiers in genetics · 2021Article
- Identifying protein complexes based on an edge weight algorithm and core-attachment structure.BMC bioinformatics · 2019Article
- A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations.BMC genomics · 2019Article
- Predicting overlapping protein complexes based on core-attachment and a local modularity structure.BMC bioinformatics · 2018Article
- CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations.BMC systems biology · 2017Article
- Developing a similarity searching module for patient safety event reporting system using semantic similarity measures.BMC medical informatics and decision making · 2017Article
- Identification of protein complexes by integrating multiple alignment of protein interaction networks.Bioinformatics (Oxford, England) · 2017Article
- Protein complex detection based on partially shared multi-view clustering.BMC bioinformatics · 2016Article
- BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.PloS one · 2016Article
- Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms.BMC bioinformatics · 2015Article
- Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future.GigaScience · 2015Review
- The relative vertex clustering value--a new criterion for the fast discovery of functional modules in protein interaction networks.BMC bioinformatics · 2015Article
- Detecting temporal protein complexes from dynamic protein-protein interaction networks.BMC bioinformatics · 2014Article
- MAE-FMD: multi-agent evolutionary method for functional module detection in protein-protein interaction networks.BMC bioinformatics · 2014Article
- Detecting overlapping protein complexes based on a generative model with functional and topological properties.BMC bioinformatics · 2014Article
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4 authors.
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Abstract
backgroundThe systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.
resultsWe have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.
conclusionThe integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.
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