Neural Network Learns to Identify Chromatid Cohesion Defects

Tokyo, Japan – Scientists from Tokyo Metropolitan University have used machine learning to automate the identification of defects in sister chromatid cohesion. They trained a convolutional neural network (CNN) with microscopy images of individual stained chromosomes, identified by researchers as having or not having cohesion defects. Automation promises better statistics, and more insight into the wide range of disorders which cause cohesion defects. The study of cohesion defects in chromosomes has been largely carried out by researchers observing the chromosomes under the microscope. They used the same technology that powers facial recognition and machine vision to analyze microscopy images of chromosomes with and without cohesion defects.