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Updated: Jun 30, 2026

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Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
Published on: March 29, 2024
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Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics
Lana Garmire1, Xiuwei Zhang2, Joshua Levy3
1The University of Alabama at Birmingham, Birmingham, Alabama 35294, United States, lgarmire@uab.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
Summary
This workshop explores artificial intelligence (AI) and machine learning advancements in single-cell spatial omics, including transcriptomics, proteomics, and metabolomics. It covers data integration, cell interaction modeling, and disease applications for precision medicine.
Area of Science:
- Computational Biology
- Genomics
- Biotechnology
Background:
- Single-cell spatial omics technologies generate high-resolution molecular data within tissue contexts.
- Integrating multi-omics data (transcriptomics, proteomics, metabolomics) is crucial for understanding cellular heterogeneity.
- Artificial intelligence (AI) and machine learning (ML) offer powerful tools for analyzing complex spatial omics datasets.

