Morphological Profiling

Morphological profiling uses image analysis to quantify the shapes and structures of cells, tissues, or organisms, aiming to reveal biological insights and predict outcomes. Current research focuses on developing automated methods using deep learning architectures like convolutional neural networks and transformers, often incorporating techniques such as instance segmentation, contrastive learning, and multi-task learning to extract meaningful features from high-content images. These advancements are improving the efficiency and accuracy of morphological profiling across diverse applications, including drug discovery, disease diagnosis (e.g., Alzheimer's, lung fibrosis), and the study of developmental processes, ultimately leading to more precise and objective biological assessments.