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Computational Systems Biology in Medicine and Biotechnology pp 87–122Cite as

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Part of the Methods in Molecular Biology book series (MIMB,volume 2399)

Abstract

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM.

Key words

  • Multi-omics
  • Multimodal
  • Metabolic modeling
  • Flux balance analysis
  • Machine learning
  • Data integration
  • Cancer survival prediction

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Acknowledgements

We would like to acknowledge the support from UKRI Research England’s THYME project, from the Children’s Liver Disease Foundation, and from Earlier.org.

Author Contributions

Conceptualization: S.V. and C.A.; Data curation: S.V., G.M. and A.O.; Formal analysis: S.V., G.M., P.M. and A.O.; Funding Acquisition: C.A.; Investigation: S.V., G.M., P.M. and A.O.; Methodology: S.V., G.M., P.M., A.O. and C.A.; Project administration: S.V. and C.A.; Resources: S.V., G.M., P.M. and A.O.; Software: S.V., G.M., P.M., A.O. and C.A.; Supervision: S.V. and C.A.; Validation: S.V., G.M. and A.O.; Visualization: S.V.; Writing—original draft: S.V., G.M., P.M., A.O. and C.A.; Writing—reviewing and editing: S.V., G.M., A.O. and C.A.

Declaration of Interests The authors declare no competing interests.

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Authors and Affiliations

  1. Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK

    Supreeta Vijayakumar, Giuseppe Magazzù & Pradip Moon

  2. Computational Systems Biology and Data Analytics Research Group, Middlebrough, UK

    Annalisa Occhipinti

  3. Centre for Digital Innovation, Teesside University, Middlesbrough, UK

    Annalisa Occhipinti

  4. Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK

    Claudio Angione

  5. Centre for Digital Innovation, Teesside University, Middlesbrough, UK

    Claudio Angione

  6. Healthcare Innovation Centre, Teesside University, Middlesbrough, UK

    Claudio Angione

Authors
  1. Supreeta Vijayakumar
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  2. Giuseppe Magazzù
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  3. Pradip Moon
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  4. Annalisa Occhipinti
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  5. Claudio Angione
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Corresponding author

Correspondence to Claudio Angione .

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Editors and Affiliations

  1. Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, MD, USA

    Dr. Sonia Cortassa

  2. Translational Gerontology Branch, Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, MD, USA

    Dr. Miguel A. Aon

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Vijayakumar, S., Magazzù, G., Moon, P., Occhipinti, A., Angione, C. (2022). A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. In: Cortassa, S., Aon, M.A. (eds) Computational Systems Biology in Medicine and Biotechnology. Methods in Molecular Biology, vol 2399. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1831-8_5

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