Scalable and universal prediction of cellular phenotypes enables in silico experiments

Abstract excerpt

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic levels. The vast combinatorial space of possible perturbations and responses makes exhaustive experimentation infeasible. Recent advances in machine learning have shown that training on diverse datasets enables transfer learning across tasks, capturing patterns that g...

Topics

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• Artificial Intelligence
• Computer Simulation
• Deep Learning
• Drug Discovery
• Drug Evaluation, Preclinical
• High-Throughput Screening Assays
• Phenotype
• Structure-Activity Relationship

Identifiers and source

Literature Corpus work

4bf7a4e6-3614-56e5-89af-422b149d47ac

DOI

10.1101/2024.08.12.607533