ArticleeLife2016
Active machine learning-driven experimentation to determine compound effects on protein patterns.
Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Critical assessment in the age of AI: challenges and opportunities for computational biology.Bioinformatics advances · 2026Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.Cancers · 2024Article
- Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Finding the most potent compounds using active learning on molecular pairs.Beilstein journal of organic chemistry · 2024Article
- Employing active learning in the optimization of culture medium for mammalian cells.NPJ systems biology and applications · 2023Article
- Active learning applied to automated physical systems increases the rate of discovery.Scientific reports · 2023Article
- A versatile active learning workflow for optimization of genetic and metabolic networks.Nature communications · 2022Article
- Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models.Journal of healthcare engineering · 2021Article
- Accelerated knowledge discovery from omics data by optimal experimental design.Nature communications · 2020Article
- Biologically Relevant Heterogeneity: Metrics and Practical Insights.SLAS discovery : advancing life sciences R & D · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.
Indexed as
Identifiers
What OpenQuestion holds
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.