Evidence-labelled mTOR research: studies, entities, pathway claims, contradictions, open questions.
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A curated, evidence-graded database of mTOR pathway research in which every claim carries its source, the conditions it was measured under, and the point where it stops holding. Studies are labelled by the kind of study behind them - from synthesis of human data down to mechanistic and in-vitro work - and traced back to their primary source, alongside a knowledge-graph view of genes, diseases, and interventions and a layer of open questions naming what the evidence does not yet resolve.
Live site: https://mtor-atlas.org
Studies are hand-selected from PubMed / Europe PMC and labelled by study design, not by quality, importance, or citation count:
These codes ran A-D until September 2026. They were renamed because a lettered ladder reads as a quality grade, which it never was, and because the old bottom tier merged primary mechanistic work with narrative reviews - two different kinds of claim. The change was prompted by an external critique from a researcher in the field; the underlying data was not re-graded, only the labels shown to readers.
A mechanistic paper is not "worse" than a trial. The code says what kind of claim a study can support, not how good it is.
Built and maintained independently by Oliver, a high-school student, together with his father Petr. Not affiliated with any lab, company, or institution. Feedback on the evidence grading, missing studies, or anything that looks wrong is very welcome - please open an issue. See CONTRIBUTING.md.
mcp/.If you use this dataset, please cite it via its Zenodo record: https://doi.org/10.5281/zenodo.22059963
A single page with all identifiers, registrations (bio.tools, FAIRsharing, GitHub, ORCID) and a ready-to-use citation is at https://mtor-atlas.org/data/.
This repository is dual-licensed, because it contains two different kinds of thing:
atlas_data/ and the generated pages - are licensed under CC BY 4.0 (see LICENSE): https://creativecommons.org/licenses/by/4.0/If you reuse the data, attribute it. If you reuse the code, MIT terms apply.