MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction
Jinxin Li, Xinguo Lu, Zihao Li, Xing Liu, Hongrui Liu, Jingjing Ruan · 2025
Read MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction by Jinxin Li, Xinguo Lu, Zihao Li, Xing Liu, Hongrui Liu, Jingjing Ruan (2025) 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-12860022.txt.
Description
Jinxin Li and co-authors build a computer tool that hunts for gene pairs that could help treat cancer. The idea is called synthetic lethality: a cell can live with one flawed gene, but not with two. A drug that blocks the partner of a cancer gene can kill the tumor and spare healthy cells. Their 2026 tool, named MGANSL, learns from many gene networks at once.
Some gene facts are shared across all the networks, while others belong to one network alone. The tool has two parts, one for each kind of fact, and they learn side by side. Tests on two human data sets show the tool beats older methods at spotting such pairs. The new pairs it finds may point drug makers to fresh targets.
Catalog record
| CREATOR | Jinxin Li, Xinguo Lu, Zihao Li, Xing Liu, Hongrui Liu, Jingjing Ruan |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |