Effect of different pretreatment methods on classification of serum samples measured with 1 H-NMR
Bibliographic record
Abstract
Abstract Extracting accurate biological information from complex datasets is a main challenge in 1H-NMR-based metabolomics research. One of the crucial steps to achieve this goal is to apply an appropriate pretreatment method before multivariate data analysis in 1H-NMR based metabolomics studies. One of the most important pretreatment methods in metabolomics studies is scaling techniques. In this study, the effect of different pretreatment approaches such as auto-, pareto-, level-, range- and vast-scaling in addition to mean-centering are investigated on both experimental and simulated datasets. The goal is linear classification modeling of the toxicity induced by different doses of graphene oxide (GO) in metabolomics context, employing partial least squares- discriminant analysis) PLS-DA). The experimental dataset includes 1H-NMR spectra of mice serum samples exposed to different doses of GO nano-sheets. Here, it is shown that type of applied pre-treatment method has a considerable effect on data analysis results. In this study, auto-, pareto- and vast-scaling lead to a better separation of classes using PLS-DA modeling and PCA. From the results of this study, it was concluded that there is no general rule for the selection of the best scaling method in the analysis of 1H-NMR metabolomics datasets, and different ways of scaling should be tested.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".