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Machine Learning for Microbial Phenotype Prediction

  • Book
  • © 2016

Overview

Authors:
  1. Roman Feldbauer

    1. Österreichisches Forschungsinstitut, OSGK, Wien, Austria

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  • Publication in the field of Bioinformatic Science
  • Includes supplementary material: sn.pub/extras

Part of the book series: BestMasters (BEST)

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Table of contents (4 chapters)

  1. Front Matter

    Pages I-XIII
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  2. Introduction

    • Roman Feldbauer
    Pages 1-24

  3. Materials and Methods

    • Roman Feldbauer
    Pages 25-39

  4. Results and Discussion

    • Roman Feldbauer
    Pages 41-77

  5. Conclusion and Outlook

    • Roman Feldbauer
    Pages 79-81

  6. Back Matter

    Pages 83-110
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Keywords

About this book

This thesis presents a scalable, generic methodology for microbial phenotype prediction based on supervised machine learning, several models for biological and ecological traits of high relevance, and the deployment in metagenomic datasets. The results suggest that the presented prediction tool can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomic studies. Unraveling relationships between a living organism's genetic information and its observable traits is a central biological problem. Phenotype prediction facilitated by machine learning techniques will be a major step forward to creating biological knowledge from big data.

Authors and Affiliations

  • Österreichisches Forschungsinstitut, OSGK, Wien, Austria

    Roman Feldbauer

About the author

Roman Feldbauer is currently employed at the Austrian Research Institute for Artificial Intelligence (OFAI) and PhD student at the University of Vienna. His research interests are machine learning, data science, bioinformatics, comparative genomics and neuroscience. In one of his current projects he investigates large biological databases in regard to the „curse of dimensionality“.

Bibliographic Information

  • Book Title: Machine Learning for Microbial Phenotype Prediction

  • Authors: Roman Feldbauer

  • Series Title: BestMasters

  • DOI: https://doi.org/10.1007/978-3-658-14319-0

  • Publisher: Springer Spektrum Wiesbaden

  • eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2016

  • Softcover ISBN: 978-3-658-14318-3Published: 24 June 2016

  • eBook ISBN: 978-3-658-14319-0Published: 15 June 2016

  • Series ISSN: 2625-3577

  • Series E-ISSN: 2625-3615

  • Edition Number: 1

  • Number of Pages: XIII, 110

  • Number of Illustrations: 29 b/w illustrations

  • Topics: Bioinformatics, Mathematical and Computational Biology, Microbiology

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