MRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom
mSphere. 2019 Jun 26;4(3):e00315-19. doi: 10.1128/mSphere.00315-19.
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.
阿图尔·亚基莫维奇(Artur Yakimovich)从事计算病毒学领域的工作,运用机器学习算法来研究宿主-病原体相互作用。在这篇《mSphere 影响力》文章中,他对两篇论文进行了反思,一篇是 Jo 等人撰写的《全息深度学习在炭疽孢子快速光学筛选中的应用》(Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606),另一篇是 Ferrari 及其同事撰写的《卷积神经网络在数字微生物成像中的细菌集落计数》(A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629-640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016)。在这里,他讨论了这些论文对他的影响,展示了人工智能算法同样适用于经典感染生物学技术和病原体的前沿无标记成像。