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Understanding the Role of (Advanced) Machine Learning in Metagenomic Workflows

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Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications (AVI-BDA 2020, ITAVIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12585))

Abstract

With the rapid decrease in sequencing costs there is an increased research interest in metagenomics, the study of the genomic content of microbial communities. Machine learning has also seen a revolution with regards to versatility and performance in the last decade using techniques like “Deep Learning”. Classical as well as modern machine learning (ML) techniques are already used in key areas within metagenomics. There are however several challenges that may impede broader use of ML and especially deep learning.

This paper provides an overview of machine learning in metagenomics, its challenges and its relationship to biomedical pipelines. Special focus is put on modern techniques such as deep learning. The results are then discussed again in the context of the AI2VIS4BigData reference model to validate its relevancy in this research area.

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Notes

  1. 1.

    Frequently the threshold is 97% [74], although there is some debate whether this number is outdated [17].

  2. 2.

    www.mg-rast.org.

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Authors and Affiliations

  1. University of Hagen, Hagen, Germany

    Thomas Krause

  2. ADAPT Centre, Munster Technological University, Cork, Ireland

    Bruno G. N. Andrade & Haithem Afli

  3. Ulster University, Belfast, Northern Ireland

    Haiying Wang & Huiru Zheng

  4. Research Institute for Telecommunication and Cooperation (FTK), Dortmund, Germany

    Matthias L. Hemmje

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  1. University of Hagen, Hagen, Germany

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  2. University of Hagen, Hagen, Germany

    Marco X. Bornschlegl

  3. Sapienza University of Rome, Rome, Italy

    Marco Angelini

  4. University of Hagen, Hagen, Germany

    Matthias L. Hemmje

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Krause, T., Andrade, B.G.N., Afli, H., Wang, H., Zheng, H., Hemmje, M.L. (2021). Understanding the Role of (Advanced) Machine Learning in Metagenomic Workflows. In: Reis, T., Bornschlegl, M.X., Angelini, M., Hemmje, M.L. (eds) Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications. AVI-BDA ITAVIS 2020 2020. Lecture Notes in Computer Science(), vol 12585. Springer, Cham. https://doi.org/10.1007/978-3-030-68007-7_4

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