Journal
TRENDS IN BIOTECHNOLOGY
Volume 37, Issue 3, Pages 310-324Publisher
CELL PRESS
DOI: 10.1016/j.tibtech.2018.08.005
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Funding
- Natural Sciences and Engineering Council of Canada
- E.W.R. Steacie Memorial Fellowship
- Canada Research Chairs program
- Canadian Institutes of Health Research Collaborative Health Research Projects program
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Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks (ANNs) leveraging modern graphics processing units (GPUs) have enabled the rapid analysis of structured input data - sequences, images, videos - to predict complex outputs with unprecedented accuracy. While there have been early successes in flow cytometry, for example, the extensive potential of pairing microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges remains largely untapped. Here we provide a roadmap to integrating deep learning and microfluidics in biotechnology laboratories that matches computational architectures to problem types, and provide an outlook on emerging opportunities.
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