Toward Graph-Based Decoding of Tumor Evolution: Spatial Inference of Copy Number Variations
Yujia Zhang, Yitao Yang, Yan Kong, Bingxu Zhong, Kenta Nakai, Hui Lu, Paola Gazzaniga · 2025
Read Toward Graph-Based Decoding of Tumor Evolution: Spatial Inference of Copy Number Variations by Yujia Zhang, Yitao Yang, Yan Kong, Bingxu Zhong, Kenta Nakai, Hui Lu, Paola Gazzaniga (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-12731904.txt.
Description
Yujia Zhang and co-authors map tumor change across space in 2025. Tumors vary from spot to spot, and that variety drives their evolution. Copy number changes, where DNA chunks are gained or lost, mark that variety. Tools borrowed from single cell work ignore where cells sit, which loses the map.
Their framework uses a graph neural network with attention layers. It learns from gene activity plus neighbor layout to infer copy changes at each spot. Multi omics features join into one spatial map of tumor variety. A fuller map can show how clones spread and where resistance may brew.
Catalog record
| CREATOR | Yujia Zhang, Yitao Yang, Yan Kong, Bingxu Zhong, Kenta Nakai, Hui Lu, Paola Gazzaniga |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 82 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |