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Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expert Skillsets
The document discusses the application of deep machine learning in automating biotech tasks, specifically focusing on unsupervised learning methods to compensate for the lack of annotated data. It highlights an example use case involving the tracking of parasite states using convolutional neural networks for individual and collective tracking. The research is presented at the FEA Research Symposium 2015 in Ghent, Belgium.
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Deep Machine Learning for Automating Biotech Tasks Through Self-Learning Expert Skillsets
- 1. http://multimedialab.elis.ugent.be Ghent University –iMinds, ELIS Department/Multimedia Lab Gaston Crommenlaan 8 bus 201 B-9050 Ledeberg – Ghent, Belgium Jasper Zuallaert, Wesley De Neve, Erik Mannens FEA Research Symposium 2015 {jasper.zuallaert, wesley.deneve, erik.mannens}@ugent.be DEEP MACHINE LEARNING FOR AUTOMATING BIOTECH TASKS THROUGH SELF-LEARNED EXPERT SKILLSETS 9th December | Ghent | Belgium Biotechnology Deep Learning Biggest successes achieved on supervised learning, requiring huge amounts of annotated data Unsupervised learning = Holy Grail Still images (e.g., whale photographs) Genomics/ Proteomics Moving images (e.g., parasite videos) How do we cope with a lack of available data and/or annotations? How can we exploit unsupervised learning to automate even more? To what extent can we automate cumbersome biotech tasks? Example use case: tracking parasite states in different compounds Train Test Predict #parasitesinparticularstate #parasitesinparticularstate t = 30 min #parasitesinparticularstate t = 60 min #parasitesinparticularstate t = 90 min Individual parasite tracking1. Based on movement rate, assign a state to each parasite Using convolutional neural networks, learn the shapes of living/dead parasites Collective parasite tracking2.Based on shape recognition, predict a state distribution at each time interval Model the evolution over time predict evolution in new compounds State-of-the-art Olympus microscope for image acquisition http://multimedialab.elis.ugent.be Ghent University – iMinds, ELIS Department/Multimedia Lab Ghent University Global Campus – Center for Biotech Data Science