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

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 a...

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Bibliographic Details
Published inBriefings in functional genomics Vol. 23; no. 5; pp. 549 - 560
Main Authors Acharya, Debabrata, Mukhopadhyay, Anirban
Format Journal Article
LanguageEnglish
Published England 27.09.2024
Subjects
Computational Biology - methods
Genomics - methods
Humans
Machine Learning
Medical Oncology - methods
Metabolomics - methods
Multiomics
Neoplasms - genetics
Neoplasms - metabolism
Precision Medicine - methods
Proteomics - methods
Patient stratification
Precision medicine
Multi-omics data integration
Precision oncology
Machine Learning
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Summary: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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ISSN:2041-2649
2041-2657
2041-2657
DOI:10.1093/bfgp/elae013