Efficient differential latent network analysis: applications to colon cancer
Yewon Han, Lee Sael · 2026
Read Efficient differential latent network analysis: applications to colon cancer by Yewon Han, Lee Sael (2026) 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/PMC13410580.txt.
Description
Yewon Han and Lee Sael present in 2026 a faster way to spot gene networks that change in colon cancer. Cancer cells rewire how genes act together, not just how much each gene is used. Older network methods capture that rewiring slowly and often miss the full picture.
Their method, shortened to EDLNA, splits gene activity data into hidden patterns for healthy and cancer samples separately. Comparing the two networks flags which gene links changed. Extra checks against protein networks and gene switches then find working modules. On test data the method ran much faster than rivals at similar accuracy. On real colon cancer data it found stage-linked gene groups that simple gene-by-gene scans missed.
Catalog record
| CREATOR | Yewon Han, Lee Sael |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |