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A Review on Machine Learning Aided Multi-omics Data Integration Techniques for Healthcare

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Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications

Part of the book series: Studies in Big Data ((SBD,volume 132))

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Abstract

To understand the mechanism of biological processes inside a human, it is necessary to look at its various regulatory aspects, such as DNA methylation and post-translational modifications of histones (PTMs). These characteristics are all susceptible to disease-induced alterations in cell signalling and phenotypes. We need to use a multi-omics approach because many illnesses result from complex processes, and we must examine each of these traits and their interactions to gain insights into the causes of diseases. Therefore, investigating multi-omics data is a crucial aspect of molecular-level healthcare research and has yielded cutting-edge discoveries. High-throughput technologies are becoming more widely available, which has led to an increase in the amount of omics data being produced. These omics data include epigenomics, transcriptomics and genomics, proteomics which all aim to represent various but complementary biological layers. By making it possible to thoroughly examine biological systems and molecular underpinnings of disease development, these data have changed healthcare research. There is a strong trend toward adding multi-omics analysis into healthcare research to explain the intricate interactions across molecular levels, even if the integration and translation of multi-omics data into relevant functional insights remains a significant barrier. Multi-omics data can help improve prevention, early detection, and prediction, monitor history, interpret patterns and design a personalised treatment. Various Machine Learning algorithms grouped under supervised and unsupervised learning techniques have been used to integrate data through various omics levels. This multi-omics analysis has various applications in deciphering the causative reason for many diseases like cancer and thus has helped in taking a step forward towards personalised medicine for tailoring the right medication for the right person. Hence, a lot of attention is given to establishing various machine learning algorithms for the automatic integration of multi-omics data. With this data, machine learning algorithms can be employed to produce diagnostic and classification biomarkers, offering fresh information. However, researchers have identified a bulk of biomarkers that consider only one omics parameter at a time and have not properly utilised a recent multi-omics research strategy, which can adequately capture the complexity of biological systems. The complementary knowledge that each omics layer contributes must be included in multi-omics data integration strategies. As a result, it is advisable to support the development of novel machine—learning methods. This chapter outlines the roadmap for multi-omics integration with machine learning, various integration methods, challenges, and future aspects.

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Authors and Affiliations

  1. Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida, 201303, Uttar Pradesh, India

    Hina Bansal, Hiya Luthra & Shree R. Raghuram

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  1. Hina Bansal
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  2. Hiya Luthra
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  3. Shree R. Raghuram
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Correspondence to Hina Bansal .

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Editors and Affiliations

  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero, Tamaulipas, Mexico

    Laura Cruz-Reyes

  3. School of Engineering, Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Universidad Tecnológica de La Habana “José Antonio Echeverría”, La Habana, Cuba

    Alejandro Rosete

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Bansal, H., Luthra, H., Raghuram, S.R. (2023). A Review on Machine Learning Aided Multi-omics Data Integration Techniques for Healthcare. In: Rivera, G., Cruz-Reyes, L., Dorronsoro, B., Rosete, A. (eds) Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications. Studies in Big Data, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-031-38325-0_10

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