Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis
Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spati...
Saved in:
Main Authors: | Yongxin Ge, Jiake Leng, Ziyang Tang, Kanran Wang, Kaicheng U, Sophia Meixuan Zhang, Sen Han, Yiyan Zhang, Jinxi Xiang, Sen Yang, Xiang Liu, Yi Song, Xiyue Wang, Yuchen Li, Junhan Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
American Association for the Advancement of Science (AAAS)
2025-01-01
|
Series: | Research |
Online Access: | https://spj.science.org/doi/10.34133/research.0568 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Statistical and computational methods for enabling the clinical and translational application of spatial transcriptomics
by: Peijun Wu, et al.
Published: (2024-12-01) -
Histological, physiological and transcriptomic analysis in hepatopancreas of Procambarus clarkii under heat stress
by: Yongfeng Zou, et al.
Published: (2025-01-01) -
Transcriptomic, histological and biochemical analyses of Macrobrachium nipponense response to acute heat stress
by: Xiao Wu, et al.
Published: (2025-01-01) -
Association of whole grain food consumption with lung cancer risk: a prospective cohort study
by: Kanran Wang, et al.
Published: (2025-01-01) -
RPRep: A Robust and Privacy-Preserving Reputation Management Scheme for Pseudonym-Enabled VANETs
by: Jin Wang, et al.
Published: (2016-03-01)