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Passing Messages between Biological Networks to Refine Predicted Interactions
Open accessAbstract
Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.
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“However, a focus on TFs is rarely sufficient, and integration of multiple data types is often required to increase the accuracy and usefulness of the resulting networks [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [13]
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“Overall, integration of multiple data types increases the accuracy of the resulting predictions [13, 147].”
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Metadata
- Authors
- Glass, K., Huttenhower, C., Quackenbush, J., & Yuan, G.-C.
- Affiliations
- Harvard University (US)
- Corresponding author
- Yuan, Guo-Cheng
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-05-31
- Volume
- 8
- Issue
- 5
- Pages
- e64832
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3669401
- OpenAlex
- W2020672230
- Cited by
- 266
- References
- 64
- Citations by year
- 2026: 24 · 2025: 26 · 2024: 56 · 2023: 22 · 2022: 24 · 2021: 29 · 2020: 24 · 2019: 15 · 2018: 11 · 2017: 12 · 2016: 12 · 2015: 7 · 2014: 4
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (1053486)
- National Institutes of Health (5r01hl111759-04)
- National Heart, Lung, and Blood Institute (R01-HL111759, P01HL105339)
- Division of Biological Infrastructure (DBI-1053486)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- regulatory network reconstruction, data integration, message passing, protein-protein interaction, gene expression, sequence motifs, yeast, genome-scale data, network prediction
- MeSH terms
- Algorithms, Computer Simulation, Models, Biological, Saccharomyces cerevisiae, Reproducibility of Results, Signal Transduction, Gene Regulatory Networks, Protein Interaction Maps
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