Editorial overview: Recent innovations in the metabolomics revolution
Bibliographic record
Abstract
Metabolism involves a complex set of chemical processes that allow organisms to transform nutrients into energy, reducing power, and the diverse range of cellular building blocks necessary for life.Although metabolism has been intensively studied for more than a century, the technology for understanding metabolic phenomena from a comprehensive, network-level perspective has only been available for a short time.The metabolomics approach -the analysis of all observable metabolites in complex biological samples -has rapidly advanced with the introduction of highresolution mass spectrometry, sophisticated chromatography, multidimensional nuclear magnetic resonance spectroscopy, clever isotope labeling strategies, and powerful software.These technological developments have dominated the field over the last two decades and have laid the foundation for the recent explosion in demand for metabolomics.In this special edition of Current Opinion in Biotechnology, we have invited a selection of both well established and emerging leaders of the metabolomics field to describe the current state-of-the-art as well as their visions for the future of metabolomics.These authors represent a cross-section of researchers who are driving the modern renaissance of metabolism research through technical innovation and biological creativity.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".