Automatic Identification of Potential Cellular Metabolites for Untargeted NMR Metabolomics
Bibliographic record
Abstract
An organism's metabolic profile provides vital information pertaining to its physiology or pathology. To monitor these biochemical changes, Nuclear Magnetic Resonance (NMR) spectroscopy has found success in non-invasively observing metabolite changes within intact samples in an untargeted manner. However, biological samples are chemically complex, comprised of many different constituents (amino acids, carbohydrates, and lipids) at varying concentrations depending on physiological and pathological conditions. Due to the narrow spectral window of proton NMR, compound resonance frequencies can often overlap, making the identification and monitoring of metabolites difficult and time consuming, particularly when dealing with large numbers of samples. Here, we introduce a Python program (ROIAL-NMR) to systematically identify potential metabolites from defined proton NMR spectral regions-of-interest (ROIs), which are identified from complex biological samples (i.e., human serum, saliva, sweat, urine, CSF, and tissues) using the Human Metabolome Database (HMDB) as a reference platform. Briefly, for disease-versus-control studies, the program considers disease types and utilizes study-defined ROIs together with their differing intensity levels, according to sample types, in differentiating disease from control to propose potential metabolites represented by these ROIs in an output table. In this report, we illustrate the utility of the program with one of our recent studies, where we measured proton NMR spectra of serum samples taken from lung cancer (LC) patients, with and without Alzheimer's disease and related dementia (ADRD). The program successfully identified 88 metabolites, with 66 differentiating LC from control patients, and 80 distinguishing LC patients with ADRD from those without ADRD to provide important information regarding pathophysiology in complex biological samples.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".