Machine learning methods for combining multiple biological data types in personalized cancer treatment: challenges and uses

Machine learning techniques are increasingly utilized for integrating multi-omics data in precision medicine.

• Multi-omics data integration includes genomics, transcriptomics, proteomics, and metabolomics combined with clinical data.
• Various machine learning methods and computational approaches have been categorized for integrating distinct omics datasets.
• The integration of multi-omics data is associated with challenges and opportunities in precision medicine and patient stratification.
• Recent advancements in deep learning and network-based approaches may enhance the harmonization of diverse biological information layers.
• A roadmap for integrating multi-omics data in precision oncology outlines potential advantages and implementation difficulties.

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