Emerging AI Approaches for Cancer Spatial Omics
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912457901998080 |
|---|---|
| author | Noorbakhsh, Javad pour, Ali Foroughi Chuang, Jeffrey |
| author_facet | Noorbakhsh, Javad pour, Ali Foroughi Chuang, Jeffrey |
| contents | Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emerging AI Approaches for Cancer Spatial Omics Noorbakhsh, Javad pour, Ali Foroughi Chuang, Jeffrey Quantitative Methods Tissues and Organs Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also new conceptual frameworks. We discuss emerging paradigms, in particular data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling, as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information. |
| title | Emerging AI Approaches for Cancer Spatial Omics |
| topic | Quantitative Methods Tissues and Organs |
| url | https://arxiv.org/abs/2506.23857 |