Training models with semi- or self-supervised learning methods is one way to reduce annotation effort since they rely on unlabeled or sparsely labeled datasets. Such approaches are particularly promising for domains with a time-consuming annotation process requiring specialized expertise and where high-quality labeled machine learning datasets are scarce, like in computational pathology. Even though some of these methods have been used in the histopathological domain, there is, so far, no comprehensive study comparing different approaches.
View Article and Find Full Text PDFClinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out.
View Article and Find Full Text PDFHerein an operationally simple multicomponent reaction of unprotected carbohydrates with amino acids and isonitriles is presented. By the extension of this Ugi-type reaction to an unprotected disaccharide a novel glycopeptide structure was accessible.
View Article and Find Full Text PDFChem Commun (Camb)
January 2014
An organocatalyzed transformation to elongate unprotected carbohydrates is described. This operationally simple methodology is based on a Knoevenagel-oxa-Michael cascade. This reaction is catalyzed by proline and DBU.
View Article and Find Full Text PDFAldol additions of unprotected carbohydrates to 1.3-dicarbonyl compounds have been described. This transformation is based on a dual activation by tertiary amines and 2-hydroxypyridine.
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