The DOI matches its Crossref record; 49 of the 53 references it cites were found in a registry or an index.
Methods for causal inference from gene perturbation experiments and validation
Open accessAbstract
Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae . The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.
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“Because gene expression data can be noisy and variational, recent advances include fusing the co-expression matrices with existing biomedical networks, such as GO annotations and PPI, and feeding the resulting graph into graph convolution layers (Section 2.2) [150, 151, 152].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [150]
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“Doing so has enabled more interpretable disease classification models (e.g., weighting gene interactions based on existing biological knowledge).”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [150]
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Inferring causation from time series in Earth system sciences
Runge, J., Bathiany, S., Bollt, E. M., Camps‐Valls, G., Coumou, D., Deyle, E. R., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marı́, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10105-3 · Cited by 1,093
Self-citationMachine learning in plant science and plant breeding
van Dijk, A. D. J., Kootstra, G., Kruijer, W., & de Ridder, D. (2020) · iScience · doi:10.1016/j.isci.2020.101890 · Cited by 340
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
Income-based variation in Sustainable Development Goal interaction networks
Lusseau, D., & Mancini, F. (2019) · Nature Sustainability · doi:10.1038/s41893-019-0231-4 · Cited by 308
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Metadata
- Authors
- Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P.
- Affiliations
- ETH Zurich (CH)
- Bern University of Applied Sciences (CH)
- University of Amsterdam (NL)
- Max Planck Institute for Intelligent Systems (DE)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2016-07-05
- Volume
- 113
- Issue
- 27
- Pages
- 7361-7368
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4941490
- OpenAlex
- W2460828264
- Cited by
- 140
- References
- 53
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 13 · 2023: 14 · 2022: 18 · 2021: 28 · 2020: 18 · 2019: 11 · 2018: 6 · 2017: 5 · 2016: 6
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI grant 639.072.410)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- causal inference, gene deletion, Saccharomyces cerevisiae, causal discovery, intervention prediction, identifiability
- MeSH terms
- Algorithms, Flow Cytometry, Models, Genetic, Saccharomyces cerevisiae, Software, Statistics as Topic, Gene Deletion
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