Deep learning in tumour genomics: from multi-omics integration to precision oncology
Zeya Zhou1, Zijin Li2, Sinuo Wang2
1International Frontier Interdiciplinary Research Institute (IFIRI), Wenzhou-Kean University , Wenzhou, People's Republic of China.
Open Biology
|June 30, 2026
Summary
Deep learning (DL) advances cancer research by analyzing tumor genomics for better mutation detection and subtype classification. This review offers a roadmap for using DL to enhance precision oncology and improve patient outcomes.
Area of Science:
- Computational biology
- Genomics
- Artificial intelligence
Background:
- Cancer is a leading global cause of death, necessitating advancements in precision medicine through genomic profiling.
- Genomic technologies and deep learning (DL) are revolutionizing cancer research and treatment strategies.
Purpose of the Study:
- To review recent advances in DL applications for tumor genomics.
- To analyze DL architectures and their impact on cancer research domains.
- To identify challenges and emerging solutions in DL for precision oncology.
Main Methods:
- Systematic analysis of DL architectures (CNNs, RNNs, GNNs, autoencoders, transformers) in cancer genomics.
- Focus on four key domains: DNA sequencing, gene expression, methylation, and multi-omics.
- Examination of technical challenges and solutions like explainable AI and federated learning.
Main Results:
- DL significantly enhances detection sensitivity for genomic alterations and improves cancer subtype classification.
- DL aids in identifying novel biomarkers and optimizing therapeutic target selection.
- Emerging DL solutions address challenges like data scarcity and model interpretability.
Conclusions:
- DL offers powerful tools for advancing precision oncology through comprehensive tumor profiling.
- Methodological innovations in DL are paving the way for more effective cancer diagnostics and therapeutics.
- This review provides a roadmap for bioinformaticians and researchers to leverage DL in cancer genomics.
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