Integrating dynamic modeling of signaling pathways with subject-specific transcriptomic data to assess breast cancer risk
Piyanut Ratphibun Yamashita, Anuwat Tangthanawatsakul, Teerasit Termsaithong, Yaowaluck Maprang Roshorm, Teeraphan Laomettachit, Andre van Wijnen, Andre van Wijnen, Andre van Wijnen · 2026
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Description
A peer-reviewed research article from Europe PMC (2026). Breast cancer is the most common cancer in women and a leading cause of death. Traditional risk assessment models, such as the Gail model, lack molecular insight, limiting their usefulness for personalized prevention strategies. We developed a computational framework that integrates individual transcriptomic data with dynamic modeling of cell signaling to create personalized models for 30 subjects (including 15 who later developed breast cancer).
Catalog record
| CREATOR | Piyanut Ratphibun Yamashita, Anuwat Tangthanawatsakul, Teerasit Termsaithong, Yaowaluck Maprang Roshorm, Teeraphan Laomettachit, Andre van Wijnen, Andre van Wijnen, Andre van Wijnen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |