Deep Learning with Microfluidics for Biotechnology. Issue 3 (March 2019)

• Record Type: Journal Article
• Authors: Riordon, Jason; Sovilj, Dušan; Sanner, Scott; Sinton, David; Young, Edmond W.K.
• Is Part Of: Trends in biotechnology. Volume 37:Issue 3(2019)
• Page Start: 310
• Page End: 324
• Publication Date: 2019-03
• Subjects: deep learning -- machine learning -- microfluidics -- lab-on-a-chip; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2018.08.005
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : 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. Highlights: High-throughput microfluidics has revolutionized biotechnology assays, enabling intriguing new approaches often at the single-cell level. Combining deep learning (to analyze data) with microfluidics (to acquire data) represents an emerging opportunity in biotechnology that remains largely untapped. Deep learning architectures have been developed to tackle raw structured data and address problems common to microfluidics applications in biotechnology. With the abundance of open-source training materials and low-cost graphics processing units, the barriers to entry for microfluidics labs have never been lower.