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Machine Learning for Metabolic Networks Modelling: A State-of-the-Art Survey

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Handbook of Machine Learning Applications for Genomics

Part of the book series: Studies in Big Data ((SBD,volume 103))

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Abstract

This chapter aims to review the machine learning algorithms and models applied for metabolic networks modeling. Metabolic models include structured repositories of information and prediction tools required to support metabolic engineering. This chapter introduces a background overview of various metabolic modeling approaches, including parametric and non-parametric models. In this chapter, we provide an overview of the various machine learning approaches used in metabolic modeling, with a focus on Hidden Markov Models (HMM), Probabilistic Context-Free Grammar (PCFG), and Bayesian Networks (BNs). We then present recent applications of machine learning in the context of metabolic network modeling concluding with a discussion on the limitations of current methods and challenges for future work.

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Authors and Affiliations

  1. University of New York Tirana, Tirana, Albania

    Marenglen Biba & Narasimha Rao Vajjhala

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  1. Marenglen Biba
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  2. Narasimha Rao Vajjhala
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Correspondence to Narasimha Rao Vajjhala .

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Editors and Affiliations

  1. Vellore Institute of Technology Universi, Vellore, India

    Sanjiban Sekhar Roy

  2. Department of Physics, Chuo University, Tokyo, Tokyo, Japan

    Y.-H. Taguchi

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Biba, M., Vajjhala, N.R. (2022). Machine Learning for Metabolic Networks Modelling: A State-of-the-Art Survey. In: Roy, S.S., Taguchi, YH. (eds) Handbook of Machine Learning Applications for Genomics. Studies in Big Data, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-16-9158-4_10

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