Exploring Human Metabolites Using the Human Metabolome Database
Bibliographic record
Abstract
The Human Metabolome Database (HMDB) is a Web-based bioinformatic/cheminformatic resource with detailed information about human metabolites and metabolic enzymes. It can be used for fields of study including metabolomics, biochemistry, clinical chemistry, biomarker discovery, medicine, nutrition, and general education. In addition to its comprehensive literature-derived data, the HMDB contains an extensive collection of experimental metabolite concentration data for plasma, urine, CSF, and/or other biofluids The HMDB is fully searchable, with many tools for viewing, sorting and extracting metabolite names, chemical structures, biofluid concentrations, enzymes, genes, NMR or MS spectra, and disease information. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, chemical structure information, physico-chemical data, reference NMR and MS spectra, normal and abnormal biofluid concentrations, tissue locations, disease associations, pathway information, enzyme data, gene sequence data, and SNP and mutation data, as well as extensive links to images, references and other public databases.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".