ArticleDisease models & mechanisms2026
A modeller's guide for biomedical discovery.
Article in Disease models & mechanisms, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Enhancing the in vitro architecture of human disease.Disease models & mechanisms · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical and computational modelling can do far more than reproduce experimental data or make predictions. When used with intent, models become instruments of discovery: they translate qualitative biological ideas into quantitative, testable hypotheses; they connect microscopic questions to macroscopic data; and, crucially, they help falsify plausible but incorrect mechanistic narratives. This Perspective explores how modelling achieves these goals. I begin by outlining why classical experimental strategies and conventional statistics sometimes fall short of addressing the mechanisms we care about. I then present a model-centred workflow for scientific discovery, using clonal lineage tracing as a running example. The second half focuses on a phenomenon that both limits and empowers model inference - universality - and explains how to turn it from a curse into an opportunity. I conclude with a concise, practical guide that distils these ideas into steps for day-to-day biomedical research.
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.