Deep learning in image-based phenotypic drug discovery
Modern drug discovery approaches often use high-content imaging to systematically study
the effect on cells of large libraries of chemical compounds. By automatically screening …
the effect on cells of large libraries of chemical compounds. By automatically screening …
Deep 3D histology powered by tissue clearing, omics and AI
A Ertürk - Nature methods, 2024 - nature.com
To comprehensively understand tissue and organism physiology and pathophysiology, it is
essential to create complete three-dimensional (3D) cellular maps. These maps require …
essential to create complete three-dimensional (3D) cellular maps. These maps require …
Learning representations for image-based profiling of perturbations
Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient
and powerful way of studying cell biology, and requires computational methods for …
and powerful way of studying cell biology, and requires computational methods for …
Rxrx1: A dataset for evaluating experimental batch correction methods
M Sypetkowski, M Rezanejad… - Proceedings of the …, 2023 - openaccess.thecvf.com
High-throughput screening techniques are commonly used to obtain large quantities of data
in many fields of biology. It is well known that artifacts arising from variability in the technical …
in many fields of biology. It is well known that artifacts arising from variability in the technical …
Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies
Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune
cells and act within the immunological synapse; an essential cell-to-cell interaction to direct …
cells and act within the immunological synapse; an essential cell-to-cell interaction to direct …
Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations
The identification of genetic and chemical perturbations with similar impacts on cell
morphology can elucidate compounds' mechanisms of action or novel regulators of genetic …
morphology can elucidate compounds' mechanisms of action or novel regulators of genetic …
Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles
Cell and organelle shape are driven by diverse genetic and environmental factors and thus
accurate quantification of cellular morphology is essential to experimental cell biology …
accurate quantification of cellular morphology is essential to experimental cell biology …
Predicting cell morphological responses to perturbations using generative modeling
Advancements in high-throughput screenings enable the exploration of rich phenotypic
readouts through high-content microscopy, expediting the development of phenotype-based …
readouts through high-content microscopy, expediting the development of phenotype-based …
Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images
A Lin, A Lu - Machine Learning in Computational Biology, 2022 - proceedings.mlr.press
Data collected by high-throughput microscopy experiments are affected by batch effects,
stemming from slight technical differences between experimental batches. Batch effects …
stemming from slight technical differences between experimental batches. Batch effects …
[HTML][HTML] Deep learning in cell image analysis
Cell images, which have been widely used in biomedical research and drug discovery,
contain a great deal of valuable information that encodes how cells respond to external …
contain a great deal of valuable information that encodes how cells respond to external …