Modular analysis of the probabilistic genetic interaction network
| العنوان: | Modular analysis of the probabilistic genetic interaction network |
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| المؤلفون: | Minghua Deng, Lin Wang, Chao Tang, Fangting Li, Lin Hou, Yunping Zhu, Dong Li, Minping Qian |
| المصدر: | Bioinformatics |
| بيانات النشر: | Oxford University Press (OUP), 2011. |
| سنة النشر: | 2011 |
| مصطلحات موضوعية: | Statistics and Probability, Saccharomyces cerevisiae Proteins, Bayesian probability, Functional genes, Saccharomyces cerevisiae, Biology, Machine learning, computer.software_genre, Biochemistry, Gene interaction, Cluster Analysis, Gene Regulatory Networks, Phosphorylation, Molecular Biology, Genetic interaction, Models, Genetic, business.industry, Systems Biology, Probabilistic logic, Computational Biology, Bayes Theorem, Construct (python library), Modular design, Original Papers, Markov Chains, Computer Science Applications, Hierarchical clustering, Computational Mathematics, Computational Theory and Mathematics, Artificial intelligence, business, computer, Algorithms |
| الوصف: | Motivation: Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Results: Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules. Contact: dengmh@pku.edu.cn; fangtingli@pku.edu.cn; zhuyp@hupo.org.cn Supplementary Information: Supplementary data are available at Bioinformatics online. |
| تدمد: | 1367-4811 1367-4803 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24e1bf95744339fd1521f0a5c145d673Test https://doi.org/10.1093/bioinformatics/btr031Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....24e1bf95744339fd1521f0a5c145d673 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13674811 13674803 |
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