[HTML][HTML] How to build the virtual cell with artificial intelligence: Priorities and opportunities

C Bunne, Y Roohani, Y Rosen, A Gupta, X Zhang… - Cell, 2024 - cell.com
Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …

Masked Image Modeling: A Survey

V Hondru, FA Croitoru, S Minaee, RT Ionescu… - arxiv preprint arxiv …, 2024 - arxiv.org
In this work, we survey recent studies on masked image modeling (MIM), an approach that
emerged as a powerful self-supervised learning technique in computer vision. The MIM task …

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

K Kenyon-Dean, ZJ Wang, J Urbanik… - arxiv preprint arxiv …, 2024 - arxiv.org
Large-scale cell microscopy screens are used in drug discovery and molecular biology
research to study the effects of millions of chemical and genetic perturbations on cells. To …

[HTML][HTML] Insights into the Identification of iPSC-and Monocyte-Derived Macrophage-Polarizing Compounds by AI-Fueled Cell Painting Analysis Tools

JB Brüggenthies, J Dittmer, E Martin, I Zingman… - International Journal of …, 2024 - mdpi.com
Macrophage polarization critically contributes to a multitude of human pathologies. Hence,
modulating macrophage polarization is a promising approach with enormous therapeutic …

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models

K Donhauser, K Ulicna, GE Moran, A Ravuri… - arxiv preprint arxiv …, 2024 - arxiv.org
Dictionary learning (DL) has emerged as a powerful interpretability tool for large language
models. By extracting known concepts (eg, Golden-Gate Bridge) from human-interpretable …

SubCell: Vision foundation models for microscopy capture single-cell biology

A Gupta, Z Wefers, K Kahnert, JN Hansen… - bioRxiv, 2024 - biorxiv.org
Cells are the functional units of life, and the wide range of biological functions they perform
are orchestrated by myriad molecular interactions within an intricate subcellular architecture …

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval

P Fradkin, P Azadi, K Suri, F Wenkel… - arxiv preprint arxiv …, 2024 - arxiv.org
Predicting molecular impact on cellular function is a core challenge in therapeutic design.
Phenomic experiments, designed to capture cellular morphology, utilize microscopy based …

Benchmarking Transcriptomics Foundation Models for Perturbation Analysis: one PCA still rules them all

I Bendidi, S Whitfield, K Kenyon-Dean… - arxiv preprint arxiv …, 2024 - arxiv.org
Understanding the relationships among genes, compounds, and their interactions in living
organisms remains limited due to technological constraints and the complexity of biological …

Exploring self-supervised learning biases for microscopy image representation

I Bendidi, A Bardes, E Cohen, A Lamiable… - Biological …, 2024 - cambridge.org
Self-supervised representation learning (SSRL) in computer vision relies heavily on simple
image transformations such as random rotation, crops, or illumination to learn meaningful …

Spherical Phenotype Clustering

L Nightingale, J Tuersley, A Cairoli, J Howes, C Shand… - bioRxiv, 2024 - biorxiv.org
Phenotypic screening experiments comprise many images of the same cells perturbed in
different ways, with biologically significant variation often subtle or difficult to see by eye. The …