ArticleNucleic acids research2013
Identifying protein interaction subnetworks by a bagging Markov random field-based method.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of interconnected markers for T-cell acute lymphoblastic leukemia.BioMed research international · 2013Pooled it
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- Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.Indian journal of pediatrics · 2017Article
- Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network.BMC bioinformatics · 2017Article
- IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer.CPT: pharmacometrics & systems pharmacology · 2017Review
- Detecting subnetwork-level dynamic correlations.Bioinformatics (Oxford, England) · 2017Article
- CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.PloS one · 2017Article
- GSNFS: Gene subnetwork biomarker identification of lung cancer expression data.BMC medical genomics · 2016Article
- Quantitative assessment of gene expression network module-validation methods.Scientific reports · 2015Article
- BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method.Bioinformatics (Oxford, England) · 2015Article
- BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.BMC genomics · 2015Article
- Proteomics for systems toxicology.Computational and structural biotechnology journal · 2014Review
- FunMod: a Cytoscape plugin for identifying functional modules in undirected protein-protein networks.Genomics, proteomics & bioinformatics · 2014Article
- EgoNet: identification of human disease ego-network modules.BMC genomics · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
Identification of differentially expressed subnetworks from protein-protein interaction (PPI) networks has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Several methods have been proposed for PPI subnetwork identification, but the dependency among network member genes is not explicitly considered, leaving many important hub genes largely unidentified. We present a new method, based on a bagging Markov random field (BMRF) framework, to improve subnetwork identification for mechanistic studies of breast cancer. The method follows a maximum a posteriori principle to form a novel network score that explicitly considers pairwise gene interactions in PPI networks, and it searches for subnetworks with maximal network scores. To improve their robustness across data sets, a bagging scheme based on bootstrapping samples is implemented to statistically select high confidence subnetworks. We first compared the BMRF-based method with existing methods on simulation data to demonstrate its improved performance. We then applied our method to breast cancer data to identify PPI subnetworks associated with breast cancer progression and/or tamoxifen resistance. The experimental results show that not only an improved prediction performance can be achieved by the BMRF approach when tested on independent data sets, but biologically meaningful subnetworks can also be revealed that are relevant to breast cancer and tamoxifen resistance.
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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.