Downloads provided by UsageCountsCellPainting-CNN model
NIH| Center for Open Bioimage Analysis ,
NSF| Collaborative Research: Image-based Readouts of Cellular State using Universal Morphology Embeddings ,
NIH| Extracting rich information from biological images
Moshkov, Nikita; Bornholdt, Michael; Benoit, Santiago; Smith, Matthew; McQuin, Claire; Goodman, Allen; Senft, Rebecca; +7 Authors
Moshkov, Nikita; Bornholdt, Michael; Benoit, Santiago; Smith, Matthew; McQuin, Claire; Goodman, Allen; Senft, Rebecca; Han, Yu; Mehrtash Babadi; Horvath, Peter; Cimini, Beth A.; Carpenter, Anne E.; Singh, Shantanu; Caicedo, Juan C.;
CellPainting-CNN model
The supplementary model for the publication "Learning representations for image-based profiling of perturbations", if you use this model, please cite our publication. It was trained on images of single-cells obtained with Cell Painting assay (488 treatments and 2 negative controls from 5 source datasets). The architechture used is EfficientNet. You can find instructions specifically related on profiling with this model in our DeepProfiler handbook. We recommend to use this model with DeepProfiler software. It is possible to use this model separately, but your Python environment should have the following package installed: efficientnet==1.1.1
- MTA Biological Research Centre Hungary
- Carnegie Mellon University United States
- Metta Development Foundation Myanmar
- Broad Institute United States
Microscopy, Single-cell analysis, Drug discovery, Image-based profiling, Cell Painting, Deep learning, Imaging assays
Microscopy, Single-cell analysis, Drug discovery, Image-based profiling, Cell Painting, Deep learning, Imaging assays
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average views 132 downloads 45 - 132views45downloads
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
Views provided by UsageCountsdownloadsDownloads provided by UsageCounts
Downloads provided by UsageCounts 0
Average
Average
Average
132
45