Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes
Cong Xue, Yi-Zhi Dai, Gui-Long Li, Yi Zhang · 2024
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Description
Cong Xue, Yi-Zhi Dai, Gui-Long Li, and Yi Zhang build a risk score for lung adenocarcinoma from genes tied to immunity and metabolism. The cancer is deadly and varied, and one-size prognosis fits badly. Gene-based scores promise finer sorting.
Training used a large public cancer atlas cohort. The team screened differing genes, ran survival filters, and shrank the list with penalized regression. Seven genes survived into the final score. A separate public cohort validated the split between high and low risk. The score was then tied to mutations, immune cell mixes, and likely drug responses. A chart form makes it usable at the desk. Scores like this aim to guide, not replace, judgment.
Catalog record
| CREATOR | Cong Xue, Yi-Zhi Dai, Gui-Long Li, Yi Zhang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |