Deep Learning with Microfluidics for Biotechnology.
Abstrak
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.
Topik & Kata Kunci
Penulis (5)
Jason Riordon
Dusan Sovilj
S. Sanner
D. Sinton
E. Young
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 184×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.tibtech.2018.08.005
- Akses
- Open Access ✓