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Cited by 245Bioinformatics and Genomic NetworksGene Regulatory Network AnalysisFungal and yeast genetics research

Combining biological networks to predict genetic interactions

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Wong, Sharyl L., Zhang, Lan V., Tong, Amy H. Y., Li, Zhijian, Goldberg, Debra S., King, Oliver D., Lesage, Guillaume, Vidal, Marc +4 more·2004·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.0406614101PMC PMC524818
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Abstract

Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.

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  • One approach to combining multiple network datasets treats them as different features annotated to each edge in a single unified network, with features combined to estimate a single interaction likelihood score for each node pair using supervised machine learning or Bayesian inference.

    “One approach is to treat the multiple datasets as different “features” annotated to each edge in a single unified network; these multiple features are combined to estimate a single interaction likelihood score for each node pair using supervised machine learning (Kramer et al., 2017) or Bayesian inference (Franceschini et al., 2013; Wong et al., 2004).”

    From Mapping the multiscale structure of biological systems (2021) · cites this paper as Wong et al., 2004

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Authors
Wong, S. L., Zhang, L. V., Tong, A. H. Y., Li, Z., Goldberg, D. S., King, O. D., Lesage, G., Vidal, M., Andrews, B., Bussey, H., Boone, C., & Roth, F. P.
Affiliations
Harvard University (US)
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2004-10-20
Volume
101
Issue
44
Pages
15682-15687
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.0406614101
PMC
PMC524818
OpenAlex
W2128923603
Cited by
245
References
49
Citations by year
2025: 8 · 2024: 5 · 2023: 4 · 2022: 7 · 2021: 5 · 2020: 5 · 2019: 6 · 2018: 6 · 2017: 5 · 2016: 7 · 2015: 10 · 2014: 6 · 2013: 16 · 2012: 13
FWCI
6.82
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation
  • Harvard University
  • Genome Canada
  • National Institutes of Health
  • National Human Genome Research Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Keywords
genetic interactions, genetic robustness, probabilistic decision trees, Saccharomyces cerevisiae, network topology, gene localization, mRNA expression, protein-protein interaction, drug target identification, cancer therapy, polygenic disease
MeSH terms
Animals, Decision Trees, Genotype, Models, Genetic, Mutation, Phenotype, Models, Statistical, Databases, Genetic

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