ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Actionable Forecasting as a Determinant of Biological Adaptation.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Spatiotemporal dynamics and forecasting of public attention to entrepreneurship education: An entropy-based modeling approach.PloS one · 2026Article
- Actionable Forecasting as a Determinant of Biological Adaptation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Organisms continuously adapt to changing environments to survive. Here, contrary to the prevailing view that predictive strategies are essential for perfect adaptation, it is shown that biological systems can precisely track their optimal state by adapting to a non-anticipatory actionable target that integrates the current optimum with its rate of change. Predictive mechanisms, such as circadian rhythms, are beneficial for accurately inferring the actionable target when environmental sensing is slow or unreliable. A new mathematical framework is developed, showing that dynamics-informed neural networks embodying these principles can efficiently capture biological adaptation even in noisy environments. These results provide fundamental insights into the interplay between forecasting, control, and inference in biological systems, redefining adaptation strategies and guiding the design of advanced adaptive biomolecular circuits.
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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.