Advancing AI for multi-omics and clinical data integration in basic and translational cancer research

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Cancer's heterogeneity requires integrated, multi-omics approaches beyond single-analyte methods.
• Combining genomics, proteomics, clinical data, and imaging offers a systems-level understanding of tumorigenesis.

Purpose of the Study:

• To highlight the role of artificial intelligence (AI) in analyzing complex, high-dimensional cancer data.
• To emphasize the importance of explainable AI for clinical translation and hypothesis generation.
• To discuss the future potential of AI in precision oncology, including digital twins.

Main Methods:

• Integration of multi-omics data (genomics, proteomics).
• Incorporation of multimodal information (clinical records, medical imaging).
• Application of artificial intelligence (AI) algorithms for data analysis and model development.

Main Results:

• AI facilitates advances in early cancer diagnosis and patient stratification.
• AI models aid in predicting therapeutic response and understanding drug resistance mechanisms.
• Explainable AI is crucial for clinical adoption and generating testable biological hypotheses.

Conclusions:

• AI-powered multi-omics integration represents a paradigm shift in precision oncology.
• Overcoming challenges in data accessibility and model generalizability is essential for widespread implementation.
• The development of patient-specific digital twins promises to revolutionize individualized cancer treatment.