Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.
Authors
Affiliations (8)
Affiliations (8)
- Artificial Intelligence Cross Disciplinary Research Institute and Faculty of medicine, Macau University of Science and Technology, Macau, China.
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- University College London, Cancer Institute, London, UK.
- Department of Orthopedics and Key Laboratory of Hepatosplenic Surgery of Ministry of Education, the First Affiliated Hospital, State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University, Harbin, China.
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University, Guangzhou, China. [email protected].
- Artificial Intelligence Cross Disciplinary Research Institute and Faculty of medicine, Macau University of Science and Technology, Macau, China. [email protected].
- Clinical Data Science Institute, Institute for Advanced Study on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, China. [email protected].
- Guangzhou National Laboratory, Guangzhou, China. [email protected].
Abstract
The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data - from genomics to proteomics - with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.