The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting lectin microarray data
Bibliographic record
Abstract
The MIRAGE (Minimum Information Required for a Glycomics Experiment) project has been established by experts in glycobiology, glycoanalytics, and glycoinformatics under the auspieces of the Beilstein-Institut. The working group aims to develop guidelines for reporting results from various experiments and analyses conducted in structural and functional studies of glycans in the scientific literature. Previous guidelines have been established for glycomic analytics, including mass spectrometry and glycan microarrays. Lectin microarrays are used worldwide for glycan profiling of various biological samples, but there are often insufficient reports on information about experimental methods such as sample preparation and fluorescence labeling. Here, we propose guidelines specifically designed to improve the standards for reporting data from lectin microarray analyses. For each of the seven areas in the workflow of a lectin microarray experiment, we provide recommendations for the minimum information that should be included when reporting results. When adopted by the scientific community the MIRAGE lectin microarray guidelines are expected to enhance data interpretation, facilitate comparison of data between laboratories and encourage the deposition of lectin microarray data in international databases.
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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 | 0.380 | 0.383 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.015 | 0.015 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".