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Using machine learning approaches for multi-omics data analysis: A review

Reel, Parminder S. ; Reel, Smarti ; Pearson, Ewan ; Trucco, Emanuele ; Jefferson, Emily

Biotechnology advances, 2021-07, Vol.49, p.107739-107739, Article 107739 [Periódico revisado por pares]

England: Elsevier Inc

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  • Título:
    Using machine learning approaches for multi-omics data analysis: A review
  • Autor: Reel, Parminder S. ; Reel, Smarti ; Pearson, Ewan ; Trucco, Emanuele ; Jefferson, Emily
  • Assuntos: Algorithms ; Biomarkers ; Data analysis ; Humans ; Machine Learning ; Metabolomics ; Multi-omics ; Predictive Modelling ; Proteomics ; Supervised Learning ; Systems Biology ; Unsupervised Learning
  • É parte de: Biotechnology advances, 2021-07, Vol.49, p.107739-107739, Article 107739
  • Notas: ObjectType-Article-2
    SourceType-Scholarly Journals-1
    ObjectType-Feature-3
    content type line 23
    ObjectType-Review-1
  • Descrição: With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies. •Machine learning methods are novel techniques to integrate omics datasets•Recently, publications based on ‘multi-omics integration’ have gained popularity•Integration of omics data using concatenation, model- or transformation-based methods•Multi-omics studies offer a more comprehensive view of complex diseases•Recommendation flowchart included for interdisciplinary professionals
  • Editor: England: Elsevier Inc
  • Idioma: Inglês
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