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Commentary | Molecular Biology and Physiology

mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

Artur Yakimovich
Artur Yakimovich
aMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
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DOI: 10.1128/mSphere.00315-19
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ABSTRACT

Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

The views expressed in this article do not necessarily reflect the views of the journal or of ASM.

  • Copyright © 2019 Yakimovich.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

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mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology
Artur Yakimovich
mSphere Jun 2019, 4 (3) e00315-19; DOI: 10.1128/mSphere.00315-19

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mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology
Artur Yakimovich
mSphere Jun 2019, 4 (3) e00315-19; DOI: 10.1128/mSphere.00315-19
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KEYWORDS

anthrax
artificial intelligence
bioimage analysis
computer vision
convolutional neural networks
deep learning
label-free imaging
machine learning

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