The Reactome Knowledgebase 2026
Bibliographic record
Abstract
The Reactome Knowledgebase (https://reactome.org) is a freely accessible, expert-curated, open-source, and open-data resource that describes human biology in molecular detail. It spans normal physiology as well as disease mechanisms, including the impact of genetic variation and drug action. Reactome content is continuously expanded and revised, with automated workflows now monitoring retracted publications to maintain data integrity. To meet the needs of a growing user base, Reactome has launched a redesigned Angular-based interface with enhanced accessibility, modular architecture, and a hierarchy of visualization tools: ReacFoam for global pathway overviews, enhanced high-level diagrams for intuitive navigation, and redesigned entity level views (ELVs) enriched with chemical structures, animated protein models, and a new "compare mode" to contrast normal and disease states. New analysis tools support multi-omics integration and customizable visualizations. Recent innovations include the React-to-me chatbot for natural language interaction, community-driven tutorials, and an open Figma icon library. Reactome's sustainability and compliance with FAIR data principles were recently recognized with CoreTrustSeal certification and its designation as a Global Core Biodata and ELIXIR resource, reinforcing its role as a trusted global knowledgebase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.090 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".