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Found 7 papers, 1 papers with code
A Decade in a Systematic Review: The Evolution and Impact of Cell Painting
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions.
Understanding Biology in the Age of Artificial Intelligence
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models.
Cell Painting Gallery: an open resource for image-based profiling
Image-based or morphological profiling is a rapidly expanding field wherein cells are "profiled" by extracting hundreds to thousands of unbiased, quantitative features from images of cells that have been perturbed by genetic or chemical perturbations.
Reproducible image-based profiling with Pycytominer
Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images.
Pseudo-Labeling Enhanced by Privileged Information and Its Application to In Situ Sequencing Images
Various strategies for label-scarce object detection have been explored by the computer vision research community.
Smart Laptop Bag with Machine Learning for Activity Recognition
In todays world of smart living, the smart laptop bag, presented in this paper, provides a better solution to keep track of our precious possessions and monitoring them in real time.
Weakly Supervised Learning of Single-Cell Feature Embeddings
Many new applications in drug discovery and functional genomics require capturing the morphology of individual imaged cells as comprehensively as possible rather than measuring one particular feature.
