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A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways

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Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2553))

Abstract

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

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Author notes

  1. Aakaanksha Kaul and Maryanne Varghese contributed equally with all other contributors.

Authors and Affiliations

  1. Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India

    Vidya Niranjan, Akshay Uttarkar, Aakaanksha Kaul & Maryanne Varghese

Authors
  1. Vidya Niranjan
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  2. Akshay Uttarkar
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  3. Aakaanksha Kaul
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  4. Maryanne Varghese
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Corresponding author

Correspondence to Vidya Niranjan .

Editor information

Editors and Affiliations

  1. Bioinformatics Institute, Agency for Science, Technology & Research, Singapore, Singapore

    Kumar Selvarajoo

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Niranjan, V., Uttarkar, A., Kaul, A., Varghese, M. (2023). A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways. In: Selvarajoo, K. (eds) Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology, vol 2553. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2617-7_19

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  • DOI: https://doi.org/10.1007/978-1-0716-2617-7_19

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2616-0

  • Online ISBN: 978-1-0716-2617-7

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