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Evaluating the util...
Evaluating the utility of brightfield image data for mechanism of action prediction
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- Harrison, Philip John (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
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- Gupta, Ankit (author)
- Uppsala universitet,Avdelningen Vi3,Bildanalys och människa-datorinteraktion
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- Rietdijk, Jonne (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
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- Wieslander, Håkan (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
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- Carreras-Puigvert, Jordi (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
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- Georgiev, Polina (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
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- Wählby, Carolina, professor, 1974- (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen Vi3
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- Spjuth, Ola, Professor, 1977- (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Science for Life Laboratory, SciLifeLab
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- Sintorn, Ida-Maria, 1976- (author)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
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(creator_code:org_t)
- Public Library of Science (PLoS), 2023
- 2023
- English.
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:7
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https://uu.diva-port... (primary) (Raw object)
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Abstract
Subject headings
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- Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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