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Carsten Joern Beese

As seen in: ACS Publications, BioRxiv
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Articles

Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data

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By David Bushiri Pwesombo, Carsten Joern Beese, Christopher Schmied, Han Sun
| dx.doi.org
1. Introduction Click to copy section linkSection link copied! The Cell Painting (CP) assay is a high-content screening method that enables an unbiased and high-throughput evaluation of pharmacological similarity among compounds by comparing the drug-induced morphological changes on cells.
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Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data

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By David Bushiri Pwesombo, Carsten Joern Beese, Christopher Schmied, Han Sun
| ACS Publications
1. Introduction Click to copy section linkSection link copied! The Cell Painting (CP) assay is a high-content screening method that enables an unbiased and high-throughput evaluation of pharmacological similarity among compounds by comparing the drug-induced morphological changes on cells.
Open in Who Shared Issue with article?

Semi-supervised Contrastive Learning for Bioactivity Prediction using Cell Painting Image Data

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By Carsten Joern Beese, Christopher Schmied, Han Sun, David Bushiri Pwesombo
| BioRxiv
Abstract Morphological profiling has recently demonstrated remarkable potential for identifying the biologi-cal activities of small molecules. Alongside the fully supervised and self-supervised machine learn-ing methods recently proposed for bioactivity prediction from Cell Painting image data, we intro-duce here a semi-supervised contrastive (SemiSupCon) learning approach.
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