Optimal Algorithm for Metabolomics Classification and Feature Selection varies by Dataset
Why this work is in the frame
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Bibliographic record
Abstract
Metabolomics, the systematic identification and quantification of all metabolites in a biological system, is increasingly applied towards identification of biomarkers for disease diagnosis, prognosis and risk prediction. Applications of metabolomics extend across the health spectrum including Alzheimer's, cancer, diabetes, and trauma. Despite the continued interest in metabolomics there are numerous techniques for analyzing metabolomics datasets with the intent to classify group membership (e.g. Control or Treated). These include Partial Least Squares Discriminant Analysis, Support Vector Machines, Random Forest, Regularized Generalized Linear Models, and Prediction Analysis for Microarrays. Each classification algorithm is dependent upon different assumptions and can potentially lead to alternate conclusions. This project seeks to conduct an in depth comparison of algorithm performance on both simulated and real datasets to determine which algorithms perform best given alternate dataset structures. Three simulated datasets were generated to validate algorithm performance and mimic 'real' metabolomics data: (Han et al., 2011) independent null dataset (no correlation, no discriminatory variables), (Davis, Schiller, Eurich, & Sawyer, 2012) correlated null (no discriminating variables), (Guan et al., 2009) correlated discriminatory. This comparison is also applied to 3 open-access datasets including two Nuclear Magnetic Resonance (NMR) and one Mass Spectrometry (MS) dataset. Performance was evaluated based on the Robustness-Performance-Trade-off (RPT) incorporating a balance between model classification accuracy and feature selection stability. We also provide a free, open-source R Bioconductor package (OmicsMarkeR) that conducts the analyses herein. The proposed work provides an important advancement in metabolomics analysis and helps alleviate the confusion of potentially paradoxical analyses thereby leading to improved exploration of disease states and identification of clinically important biomarkers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it