Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad a...

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Vydané v:	Analytical and bioanalytical chemistry Ročník 414; číslo 21; s. 6379 - 6391
Hlavní autori:	Bischoff, Daniel, Walla, Brigitte, Weuster-Botz, Dirk
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022 Springer Springer Nature B.V
Predmet:	Algorithms Analysis Analytical Chemistry automation Biochemistry Biomolecules Biotechnology Characterization and Evaluation of Materials Chemistry Chemistry and Materials Science chromatography Cognitive tasks Computer applications Crystallization Crystalloids (Botany) Crystals data collection Datasets Deep learning Food Science Laboratory Medicine Learning algorithms Machine learning Methods Micrography Molecular biology Monitoring Monitoring/Environmental Analysis Photography Photomicrographs Protein purification Proteins Purification purification methods Ray tracing Research Paper Single crystals Structure Sustainability in (Bio-)Analytical Chemistry Synthetic data Germany Deep learning Synthetic data sets Protein crystallization Automated image analysis
ISSN:	1618-2642, 1618-2650, 1618-2650
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