ArticleNature communications2020
Accelerated knowledge discovery from omics data by optimal experimental design.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- BIOGEN: evidence-grounded multi-agent reasoning framework for transcriptomic interpretation in antimicrobial resistance.Frontiers in bioinformatics · 2026Article
- Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.Cell genomics · 2025Review
- Digital psychiatry: concepts, framework, and implications.Frontiers in psychiatry · 2025Review
- Traditional or adaptive design of experiments? A pilot-scale comparison on wood delignification.Heliyon · 2024Article
- Review
- A blueprint for a synthetic genetic feedback optimizer.Nature communications · 2023Article
- Algorithmic lifestyle optimization.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Article
- Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models.MRS bulletin · 2022Article
- Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning.Frontiers in artificial intelligence · 2022Article
- Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters.Life (Basel, Switzerland) · 2021Article
- A forecast for large-scale, predictive biology: Lessons from meteorology.Cell systems · 2021Review
- Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources.Life (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.
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Registered trials
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.