Artificial intelligence for high content imaging in drug discovery

Carreras-Puigvert, Jordi; Spjuth, Ola

doi:10.1016/j.sbi.2024.102842

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2024

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Cited by 5 publications

References 69 publications

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Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

Ahmad,

Bano,

Sharma

et al. 2024

Studies in Computational Intelligence

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Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

Ahmad,

Bano,

Sharma

et al. 2024

Studies in Computational Intelligence

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Explainable biology for improved therapies in precision medicine: AI is not enough

Jurisica

2024

Best Practice & Research Clinical Rheumatology

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Evaluating batch correction methods for image-based cell profiling

Arevalo,

Su,

Ewald

et al. 2024

Nat Commun

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High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.

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Artificial intelligence for high content imaging in drug discovery

Cited by 5 publications

References 69 publications

Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

Generative AI in Drug Designing: Current State-of-the-Art and Perspectives

Explainable biology for improved therapies in precision medicine: AI is not enough

Evaluating batch correction methods for image-based cell profiling

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