Deep learning multi-omics integration identifies new molecular subtypes of lung cancer
Bianca Gonda, Derek Wang, Sayed Mehedi Azim, Gabriele Romano, Iman Dehzangi · 2026
Read Deep learning multi-omics integration identifies new molecular subtypes of lung cancer by Bianca Gonda, Derek Wang, Sayed Mehedi Azim, Gabriele Romano, Iman Dehzangi (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/pmc-13371118.txt.
Description
Bianca Gonda and four co-authors use deep learning to find new lung cancer subtypes in a 2026 paper. Lung tumors differ widely in their molecular makeup, which shapes growth and patient outcomes. Large public data sets now hold gene, mutation, protein, and clinical records side by side.
Most past work grouped tumors using just one data type. This pipeline merges gene activity with mutation data instead. An autoencoder network compresses the combined data, and model-based clustering sorts tumors into groups. The approach seeks subtypes that single-view studies would miss. Clearer subtypes could guide prognosis and match patients to better-targeted care in the future. The study adds a fresh route for multi-data cancer analysis.
Catalog record
| CREATOR | Bianca Gonda, Derek Wang, Sayed Mehedi Azim, Gabriele Romano, Iman Dehzangi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |