Integrative spatial omics and artificial intelligence: transforming cancer research with omics data and AI

Area of Science:

• Biomedical research
• Computational biology
• Oncology

Background:

• Multi-omics and spatial data integration offers insights into cellular functions and disease mechanisms.
• High-dimensional omics data presents interpretation and clinical translation challenges.
• Artificial intelligence (AI) and machine learning (ML) are crucial for analyzing complex biological datasets.

Purpose of the Study:

• To review advancements in spatial Omics (SO) and AI-driven computational models for oncology.
• To highlight methodologies and platforms for spatial transcriptomics (ST) and spatial proteomics (SP).
• To discuss AI applications in interpreting spatial omics data for predictive modeling and personalized medicine.

Main Methods:

• Review of spatial Omics (SO) techniques like spatial barcoding, in situ sequencing, and digital spatial profiling.
• Exploration of AI-driven computational models including deep learning and spatial graph-based analyses.
• Discussion of advanced mathematical frameworks like spatial graph theory and topological data analysis.

Main Results:

• AI and SO enhance interpretation of high-dimensional spatial omics data.
• These integrated approaches facilitate biomarker discovery and personalized therapeutic strategies in cancer.
• Challenges include data complexity, computational demands, and standardization, requiring advanced analytical frameworks.

Conclusions:

• Integrating ST, SP, and AI is key to advancing precision oncology.
• Future research should focus on improving spatial resolution, data harmonization, and AI predictive models.
• This integration promises dynamic, patient-specific treatment strategies and improved understanding of cancer.