Machine learning and computer vision approaches for phenotypic profiling

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.

# Cell Image Analysis Techniques # Biochemistry, Genetics and Molecular Biology # Single-cell and spatial transcriptomics # Digital Imaging for Blood Diseases # Computer Science

Ben T. Grys, Dara S. Lo, Nil Sahin, et al. (2016). "Machine learning and computer vision approaches for phenotypic profiling". The Journal of Cell Biology. https://doi.org/10.1083/jcb.201610026