Deep learning in image-based phenotypic drug discovery

D Krentzel, SL Shorte, C Zimmer - Trends in Cell Biology, 2023 - cell.com
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 …

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 …

Learning representations for image-based profiling of perturbations

N Moshkov, M Bornholdt, S Benoit, M Smith… - Nature …, 2024 - nature.com
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 …

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 …

Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies

S Shetab Boushehri, K Essig, NK Chlis, S Herter… - Nature …, 2023 - nature.com
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 …

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations

SN Chandrasekaran, BA Cimini, A Goodale, L Miller… - Nature …, 2024 - nature.com
The identification of genetic and chemical perturbations with similar impacts on cell
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

J Burgess, JJ Nirschl, MC Zanellati, A Lozano… - Nature …, 2024 - nature.com
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 …

Predicting cell morphological responses to perturbations using generative modeling

A Palma, FJ Theis, M Lotfollahi - Nature Communications, 2025 - nature.com
Advancements in high-throughput screenings enable the exploration of rich phenotypic
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 …

[HTML][HTML] Deep learning in cell image analysis

J Xu, D Zhou, D Deng, J Li, C Chen, X Liao… - Intelligent …, 2022 - spj.science.org
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 …