Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer
Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian · 2024
Read Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer by Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11655766.txt.
Description
Tian and co-authors report a 2024 study that hunts gene markers for non-small cell lung cancer with machine learning. This cancer is common, and five-year survival stays near a fifth of patients. Markers must work across ages to be useful in clinics. The team split tumor samples from a public database into three age bands, all over forty.
Four learning methods each picked gene sets with strong signal in every band. The overlap of the sets pointed to one gene, CAT, as the shared winner. The authors then checked what the gene says about patient outlook. They present CAT as a fresh candidate for diagnosis and prognosis in this cancer.
Catalog record
| CREATOR | Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |