BioMedTools: a language model-powered community for biomedical computational tools
Bibliographic record
Abstract
Abstract A large number of biomedical computational tools have spawned several tool registries. However, in the face of the rapid growth in the number of tools, existing tool registries, which are manually curated or community-driven, are difficult to keep up to date, resulting in inadequate tool repository data. In this paper, we show that language models (LMs) can aid in building a community of tools. We introduce BioMedTools ( https://biomed.tools ), a community of biomedical computational tools that mainly implements LM-based tool identification and a chat assistant. Compared with existing tool registries, BioMedTools achieves excellence in terms of the number of tools, frequency of data updates, and functionality. Meanwhile, the Model Context Protocol (MCP) servers hub in BioMedTools may promote the building of agents in the biomedical field. BioMedTools enables the efficient collection of tools and enhances their findability and accessibility.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".