Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing
Md Feroj Ahmed, Ovi Sharma, Tasfia Noor, Md Abdul Latif, Md Foysal Ahmed, Moumita Bashak, Dewan Mumdood Ahmed, Md Sanoar Hossain, Md Nurul Haque Mollah · 2026
Read Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing by Md Feroj Ahmed, Ovi Sharma, Tasfia Noor, Md Abdul Latif, Md Foysal Ahmed, Moumita Bashak, Dewan Mumdood Ahmed, Md Sanoar Hossain, Md Nurul Haque Mollah (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/PMC13528018.txt.
Description
Md Feroj Ahmed and eight colleagues mine gene data in a 2026 study for links between type 2 diabetes and triple-negative breast cancer. The two diseases often occur together, and their drugs can clash. The team combined gene activity datasets for both conditions. They used an explainable machine learning method with SHAP values to rank shared genes.
Thirty-six shared genes separated patients from controls in both diseases. Four stood out as key shared genes: S100A9, CIRBP, USP10, and PSMD1. Network analysis found the switches that control them. Pathway analysis pointed to common disease mechanisms. Docking tests then screened known drugs against the key genes. Two candidates emerged, tepotinib and ursodiol, as possible shared treatments. The authors offer them as leads for lab testing, not proven therapies.
Catalog record
| CREATOR | Md Feroj Ahmed, Ovi Sharma, Tasfia Noor, Md Abdul Latif, Md Foysal Ahmed, Moumita Bashak, Dewan Mumdood Ahmed, Md Sanoar Hossain, Md Nurul Haque Mollah |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59500 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |