Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions
Neelam Sharma, Rabia Şen, Cemal Erdem · 2026
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Description
Gene activity maps keep each measurement's place in the tissue, and they have outgrown hand analysis. A 2026 review by Neelam Sharma, Rabia Şen, and Cemal Erdem surveys the artificial intelligence tools built for them. The review explains the mapping methods and public datasets first, then walks the full workflow.
Machine learning and deep learning now handle cleaning, cell boundary drawing, region finding, and pattern spotting. They also help merge these maps with other data types. The authors cover large language models used to interpret results. They close with open problems and directions for researchers entering a fast growing field.
Catalog record
| CREATOR | Neelam Sharma, Rabia Şen, Cemal Erdem |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 106 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |