Top-Down Lipidomic Screens by Multivariate Analysis of High-Resolution Survey Mass Spectra
Bibliographic record
Abstract
Direct profiling of total lipid extracts on a hybrid LTQ Orbitrap mass spectrometer by high-resolution survey spectra clusters species of 11 major lipid classes into 7 groups, which are distinguished by their sum compositions and could be identified by accurately determined masses. Rapid acquisition of survey spectra was employed as a "top-down" screening tool that, together with the computational method of principal component analysis, revealed pronounced perturbations in the abundance of lipid precursors within the entire series of experiments. Altered lipid precursors were subsequently identified either by accurately determined masses or by in-depth MS/MS characterization that was performed on the same instrument. Hence, the sensitivity, throughput and robustness of lipidomics screens were improved without compromising the accuracy and specificity of molecular species identification. The top-down lipidomics strategy lends itself for high-throughput screens complementing ongoing functional genomics efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".