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License: CC BY 4.0

551 to 560 of 865 Results

README.txt Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells Plain Text - 3.2 KB - MD5: f08738d456897bb952c2cd4bdcb927d7
UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images Jan 16, 2022 - Keemia instituudi andmed Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veiksina, Santa; Laasfeld, Tõnis; Rinken, Ago, 2022, "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images", https://doi.org/10.23673/RE-304, DATADOI, V1 The "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images" dataset contains the machine learning model files for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images. Random forest-based models are implemented as Ilastik projects while deep-learning models are...
migratsioonieelne_statistika.html Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images HTML - 907 B - MD5: c07b6daef3dbee864bf87e6aa836cde2 2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid. Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent...
README.txt Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images Plain Text - 5.5 KB - MD5: 8ea32d6fc6608c9bb88f3f58393234ff README
RF-BF-1.zip Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images ZIP Archive - 466.9 MB - MD5: 0e3491e2653da38cb1b3c73146a6cdad Random forest based bright-field image cell segmentation model as Ilastik model
RF-BF-2.zip Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images Unknown - 1022.6 MB - MD5: aa0b7ff5cf8f238c870cd2ab01567ba5 Random forest based bright-field image cell segmentation model as Ilastik model
RF-FL-1.zip Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images ZIP Archive - 154.0 MB - MD5: 909c653b0327db87759cd487debadda0 Random forest based fluorescence image cell segmentation model as Ilastik model
U-Net3-BF-1.zip Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images ZIP Archive - 13.7 MB - MD5: 56fb5e9106cbfbb35df1a1ba7336825e U-Net3 based bright-field image cell segmentation model as Keras model
U-Net3-FL-1.zip Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images ZIP Archive - 14.2 MB - MD5: 1a1aecbbb79cb68beeb6fbf9b6e0f011 U-Net3 based fluorescence image cell segmentation model as Keras model
UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth Jan 16, 2022 - Keemia instituudi andmed Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veiksina, Santa; Laasfeld, Tõnis; Rinken, Ago, 2022, "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth", https://doi.org/10.23673/RE-305, DATADOI, V1 The "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth" dataset contains the raw microscopy images, corresponding bright-field Z-stack based contrast-enhanced images and the corresponding manual ground truth for fluorescence and bright-field images as well as ground truth generated from...

README.txt

Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells

Plain Text - 3.2 KB -

UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

Jan 16, 2022 - Keemia instituudi andmed

Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veiksina, Santa; Laasfeld, Tõnis; Rinken, Ago, 2022, "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images", https://doi.org/10.23673/RE-304, DATADOI, V1

The "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images" dataset contains the machine learning model files for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images. Random forest-based models are implemented as Ilastik projects while deep-learning models are...

migratsioonieelne_statistika.html

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

HTML - 907 B -

2026. aasta migratsiooni käigus varasemast DataDOI süsteemist üle kantud kasutusstatistika kajastab tegevust eelmises DSpace-põhises süsteemis ega näita Dataverse’i uusi kasutusandmeid. Usage statistics carried over from the previous DataDOI system as part of the 2026 migration reflect activity in the former DSpace-based system and do not represent...

README.txt

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

Plain Text - 5.5 KB -

README

RF-BF-1.zip

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

ZIP Archive - 466.9 MB -

Random forest based bright-field image cell segmentation model as Ilastik model

RF-BF-2.zip

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

Unknown - 1022.6 MB -

Random forest based bright-field image cell segmentation model as Ilastik model

RF-FL-1.zip

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

ZIP Archive - 154.0 MB -

Random forest based fluorescence image cell segmentation model as Ilastik model

U-Net3-BF-1.zip

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

ZIP Archive - 13.7 MB -

U-Net3 based bright-field image cell segmentation model as Keras model

U-Net3-FL-1.zip

Jan 16, 2022 - UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

ZIP Archive - 14.2 MB -

U-Net3 based fluorescence image cell segmentation model as Keras model

UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth

Jan 16, 2022 - Keemia instituudi andmed

Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veiksina, Santa; Laasfeld, Tõnis; Rinken, Ago, 2022, "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth", https://doi.org/10.23673/RE-305, DATADOI, V1

The "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth" dataset contains the raw microscopy images, corresponding bright-field Z-stack based contrast-enhanced images and the corresponding manual ground truth for fluorescence and bright-field images as well as ground truth generated from...

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