A group of researchers, including Dr Chee Siang (Jim) Ang, Senior Lecturer at the School of Computing, Dr Gianluca Marcelli (School of Engineering and Digital Arts), and Professor Martin Michaelis and Dr Mark Wass (School of Biosciences), have developed a computer algorithm to identify differences in cancer cell lines based on microscopic images, preventing the misclassification of cells.
The research, published in an article entitled ‘Towards image-based cancer cell lines authentication using deep neural networks‘ in the journal Scientific Reports, reports that the computer algorithm is a key development towards ending the misidentification of cells in laboratories. The researchers say that this breakthrough has the potential to provide an easy-to-use tool that enables the rapid identification of all cell lines in a laboratory without expert equipment and knowledge.
In an article for Drug Target Review Dr Ang said: ‘our collaboration has demonstrated tremendous results for potential future implementation in laboratories and within cancer research. Utilising this new algorithm will yield further results that can transform the format of cell identification in science, giving researchers a better chance of correctly identifying cells, leading to reduced error in cancer research and potentially saving lives. The results also show that the computer models can allocate exact criteria used to identify cell lines correctly, meaning that the potential for future researchers to be trained in identifying cells accurately may be greatly enhanced too’.
The full research article can be read on the publisher’s website, here: