A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Apr 11, 2024Briefings in functional genomics

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

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Abstract

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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Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.

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Abstract

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