Part of the Oxford Instruments Group

Applying Oxford Machine Learning in Cell Biology

Deep Learning sits within the wider category of machine learning methods and has received a lot of attention, including recently for driverless cars. Central to deep learning is the use of deep neural networks that can be trained, using labelled examples, to perform many complex tasks with a high degree of accuracy.

There are many complex questions within life sciences – from developmental studies, to many aspects of brain function. Deep learning is rapidly gaining interest across many areas of life science research to tackle such challenges in a way that was not previously possible. Some areas where deep learning has proven useful include: in single-cell RNA sequencing so that the individual cells can be monitored during development and related to the whole organism, helping to model pathways and functions within the brain, and in the detection of cancerous cells. Like many of the developments outlined in this article it will take some time before it is clear how and where this technology can best be utilised.

sCMOS cameras are found within the imaging set-up of many of these studies and play a key unseen role as it is important to feed accurate image data into these systems. Given that many of these applications involve fluorescence microscopy, sCMOS cameras are perfectly suited.


  • Eraslan, G., Avsec, Ž., Gagneur, J. et al. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20, 389–403 (2019) doi:10.1038/s41576-019-0122-6
  • Munir, K.; Elahi, H.; Ayub, A.; Frezza, F.; Rizzi, A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers 2019, 11, 1235.
  • Christiansen et al., 2018, Cell 173, 792–803 April 19, 2018 ª 2018 Elsevier Inc.

Date: February 2020

Author: Dr Alan Mullan and Dr Claudia Florindo

Category: Application Note

Download as pdf


Related assets