The MassBank contributions of the mFam Consortium
Bibliographic record
Abstract
The analysis of metabolic profiles using high resolution mass spectrometry (MS) data gives deep insights into the biological processes. In metabolomics, MS generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a family, evaluating MS features at the metabolite family level can help assign functional roles to individual MS features. This data can help interpret metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is very important to have good quality, reliable, and diverse spectral libraries. We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from all biological origins, including mammals, microorganisms, and plants. The mFAM-MS/MS Collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites. The mFam Consortium used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories and leverages 12 instrument types, including QTOF, Orbitrap, and Ion Mobility systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.097 | 0.095 |
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".