Multi-algorithm machine learning combined with in silico gene knockout reveals the diagnostic value and functional regulatory networks of ferroptosis-related genes in gastric cancer
Wen Ma, Ke Liu, Lei Wang · 2026
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Description
Wen Ma, Ke Liu, and Lei Wang combine many machine learning algorithms with simulated gene knockouts to find diagnostic genes in stomach cancer. Their 2026 study focuses on genes tied to ferroptosis, an iron-driven cell death process shaping this cancer.
Intersecting several gene sets yielded fifty-three candidate genes enriched in ferroptosis and tumour pathways. Over one hundred algorithm combinations competed to build the best early-screening model. The winning five-gene model scored extremely high accuracy in training and in two independent validation groups. Single-cell data showed where the five genes act in tumours. Computer knockouts then probed the functional networks these diagnostic genes may control downstream.
Catalog record
| CREATOR | Wen Ma, Ke Liu, Lei Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |