XGBoost- and Mass Spectrometry-Based Feature Selection for Identifying Metabolic Biomarkers Associated with HBV-Related Liver Disease Progression and Hepatocellular Carcinoma Treatment
Bibliographic record
Abstract
XGBoost, a gradient boosting algorithm, is widely recognized for its efficiency and robustness in multiclass classification tasks. Metabolomics serves as a powerful tool for biomarker discovery; however, metabolic biomarkers associated with the progression from chronic hepatitis B (CHB) to liver cirrhosis (LC) to hepatocellular carcinoma (HCC), as well as those related to treatment effects in HCC (HCCAT), remain unclear. In this study, an XGBoost-based machine learning approach combined with mass spectrometry was used to analyze the metabolic profiles of 30 healthy controls (HC), 29 CHB patients, 30 LC patients, 30 HCC patients, and 30 HCCAT patients. Biomarker screening was conducted through three comparative analyses: (1) HC, CHB, LC, HCC, and HCCAT; (2) HC, CHB, LC, and HCC; and (3) HC, HCC, and HCCAT. A total of 17 metabolic biomarkers were identified, among which nine had not been previously associated with HBV-related liver diseases. Notably, a potential biomarker panel composed of eicosenoic acid, dihydromorphine, cysteine, acetic acid, sitosterol, and hypoxanthine showed promise for disease prognosis and therapeutic evaluation. These findings highlight the great potential of integrating metabolomics with machine learning to identify novel metabolic biomarkers related to HBV-associated liver disease progression and treatment response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".