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491 to 500 of 523 Results
210607_192054_CHO-K1-hM4R-UR-CG072-competition_full.zip
Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells
ZIP Archive - 6.0 GB - MD5: 5943b633110a360103a460930456524e
210608_162557_CHO-K1-hM4R-UR-CG072-competition_full.zip
Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells
ZIP Archive - 6.0 GB - MD5: 51a188e63691371896bc5e712e2c148a
210621_162328_CHO-K1-hM4R-and-CHO-K1-UR-CG072-saturation.zip
Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells
ZIP Archive - 8.3 GB - MD5: efde2e170a2379e5bc9b94dcfda7591e
210622_154926_CHO-K1-hM4R-UR-CG072-competition_full.zip
Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells
ZIP Archive - 2.0 GB - MD5: 748762eef6919e197408ed2ebd2ee8f3
migratsioonieelne_statistika.html
Jan 19, 2022 - UT-GPCR001 microscopy of ligand binding to M4 muscarinic receptor in live CHO-K1-hM4 cells
HTML - 3.0 KB - MD5: 4d0d1dc275c349ebcff04cb234ccc488
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 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
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