Combining Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry in Biomarker Discovery
Bibliographic record
Abstract
Metabolic profiling of biological specimens is emerging as a promising approach for discovering specific biomarkers in the diagnosis of a number of diseases. Amongst many analytical techniques, nuclear magnetic resonance spectroscopy and mass spectrometry are the most information-rich tools that enable high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in a majority of metabolomics applications, there is a growing interest in combining the data from the two methods to effectively unravel the mammoth complexity of biological samples. In this article, current developments in nuclear magnetic resonance, mass spectrometry and multivariate statistical analysis methods are described. While some general applications that utilize the combination of the two analytical methods are presented briefly, the emphasis is laid on the recent applications of nuclear magnetic resonance and mass spectrometry methods in the studies of hepatopancreatobiliary and gastrointestinal malignancies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".