MSphere of Influence: the Rise of Artificial Intelligence in Infection Biology
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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.
Toward the novel AI tasks in infection biology.
Yakimovich A mSphere. 2024; 9(2):e0059123.
PMID: 38334404 PMC: 10900907. DOI: 10.1128/msphere.00591-23.
Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells.
Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Witte R, Puntener D iScience. 2021; 24(6):102543.
PMID: 34151222 PMC: 8192562. DOI: 10.1016/j.isci.2021.102543.