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Integrative multi-omics and machine learning/deep learning approaches in cancer knowledge discovery: A scoping review
Nada Benabbou1, Mounia Abik1, Shakuntala Baichoo2
1Software Engineering Department, Ensias, Mohammed V University, Rabat, Morocco.
Cancer Treatment and Research Communications
|February 25, 2026
Summary
Artificial intelligence (AI) in cancer research, using machine learning (ML) and deep learning (DL) with multiomics data, shows promise. Standardization of AI integration methods is needed for better cancer prediction and subtyping.
Area of Science:
- Oncology and Computational Biology
- Artificial Intelligence in Medicine
- Bioinformatics and Data Science
Background:
- The application of AI, specifically ML and DL, is rapidly advancing cancer research and data analysis.
- Integrating multiomics data (genomics, transcriptomics, proteomics) offers a comprehensive approach to understanding cancer complexity.
- There is a growing need to evaluate current methods for multiomics integration in oncology.
Conclusions:
- Multiomics integration, powered by AI, is vital for advancing cancer understanding, prediction, and classification.
- The current literature reveals a need for greater standardization in multiomics integration methods.
- Further research and standardization are essential for robust AI-driven clinical applications in oncology.
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