Related Experiment Videos
Artificial Intelligence-Driven Multiomics Integration in Lung Cancer: From Data Convergence to Precision Phenomics
Sanjukta Dasgupta1, Debapriya De1
1Department of Biotechnology, Brainware University, Kolkata, India.
Omics : a Journal of Integrative Biology
|August 7, 2026
Summary
Artificial intelligence (AI) is revolutionizing lung cancer research by integrating multiomics data. This approach enhances molecular subtyping, biomarker discovery, and personalized treatment strategies for improved patient outcomes.
Area of Science:
- Oncology
- Bioinformatics
- Artificial Intelligence
Background:
- Lung cancer causes significant mortality due to molecular diversity and treatment resistance.
- High-throughput omics technologies offer deep tumor characterization but single-omics data provide fragmented insights.
- Integrative multiomics approaches are crucial for a comprehensive understanding of lung cancer biology.
Purpose of the Study:
- To review recent advancements in AI-driven multiomics integration for lung cancer.
- To highlight AI applications in molecular subtyping, biomarker discovery, and prognosis prediction.
- To discuss challenges and future directions in AI-powered lung cancer research.
Main Methods:
- Review of recent literature on AI and multiomics in lung cancer.
- Focus on machine learning and deep learning for data integration.
- Exploration of applications in precision oncology and personalized management.
Main Results:
- AI effectively integrates heterogeneous multiomics data for lung cancer.
- AI facilitates molecular subtyping, biomarker discovery, and therapeutic response prediction.
- Emerging strategies integrate radiomics and digital pathology with multiomics data.
Conclusions:
- AI-driven multiomics integration holds transformative potential for lung cancer research.
- Precision phenomics offers a framework for personalized cancer management.
- These approaches promise to improve patient outcomes and advance precision oncology.
