Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology
Recent advances in spatial omics technologies have enabled new approaches for analyzing tissue morphology, cell composition, and biomolecule expression patterns in situ. These advances are promoting the development of new computational tools and quantitative techniques in the emerging field of digit...
Saved in:
| Main Authors: | Hojung Kim, Jina Kim, Su Yeon Yeon, Sungyong You |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-11-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1465098/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Entropy measures for quantifying complexity in digital pathology and spatial omics
by: Xiao Li, et al.
Published: (2025-06-01) -
Nanomedicines for the treatment of genitourinary neoplasms
by: Shaowen Wang, et al.
Published: (2025-10-01) -
Liquid Biopsies in Genitourinary Malignancies
by: Roger Li
Published: (2023-07-01) -
Health-care distribution of patients with genitourinary birth defects in Colombia: a spatial analysis of registered cases
by: Jessica Santander, et al.
Published: (2024-10-01) -
Primary lymphomas of the genitourinary tract: A population-based study
by: Carlotta Palumbo, et al.
Published: (2020-10-01)