A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer
Xiaoyu Ye, Jialiang Xing, Limin Niu, Shanshan Ding, Xingguo Song · 2026
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Description
Xiaoyu Ye and colleagues build in 2026 a blood test model that uses chemical fingerprints to detect stomach cancer. Serum from one hundred fifty-one patients and one hundred three healthy controls was profiled with mass spectrometry. Machine learning methods then narrowed thousands of measured compounds down to a small diagnostic panel.
Four compounds formed the final signature, and a random forest model separated patients from controls with very high accuracy on held-out data. The model beat standard blood markers and stayed well calibrated in checks reported by the team. Explanations of the model highlighted shifts in fat and amino acid pathways. A noninvasive test built this way could support earlier detection, though outside validation is still ahead.
Catalog record
| CREATOR | Xiaoyu Ye, Jialiang Xing, Limin Niu, Shanshan Ding, Xingguo Song |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |