The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Bridging The Gap Between Machine Learning and the Life Sciences with Artur Yakimovich - #411
Sep 21, 2020
In this engaging discussion, Artur Yakimovich, co-founder at Artificial Intelligence for Life Sciences and visiting scientist at University College London, dives into the fascinating intersection of AI and life sciences. He shares insights about the challenges in merging biology with computational tools, focusing on his innovative use of deep learning and capsule networks. Artur also highlights the importance of community in this interdisciplinary field, promoting collaboration to tackle complex biological problems, and sheds light on the exciting advancements in virus visualization and data analysis.
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AI Tool Applicability in Life Sciences
- Commoditized AI tools are not always applicable to life science problems.
- Life scientists integrate diverse data types, unlike computer scientists who focus on specific problems.
Microscopy and Biomedical Imaging's Increasing Digitization
- Microscopy and biomedical imaging are central to quantitative biology, becoming increasingly digitized.
- New tools expand the possibilities of working with images, but also the required knowledge.
Deep Learning with Super-Resolution Microscopy
- Artur Yakimovich's postdoc work involved deep learning with super-resolution microscopy of vaccinia virus.
- He used advanced neural networks to analyze virus behavior inside cells, employing "mimicry embedding" to improve results.