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Sökning: WFRF:(Engel Christiane)

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1.
  • Allesøe, Rosa Lundbye, et al. (författare)
  • Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
  • 2023
  • Ingår i: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
  • Tidskriftsartikel (refereegranskat)abstract
    • The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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2.
  • Hamann, Joerg, et al. (författare)
  • International Union of Basic and Clinical Pharmacology. XCIV. Adhesion G Protein-Coupled Receptors
  • 2015
  • Ingår i: Pharmacological Reviews. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0031-6997 .- 1521-0081. ; 67:2, s. 338-367
  • Forskningsöversikt (refereegranskat)abstract
    • The Adhesion family forms a large branch of the pharmacologically important superfamily of G protein-coupled receptors (GPCRs). As Adhesion GPCRs increasingly receive attention from a wide spectrum of biomedical fields, the Adhesion GPCR Consortium, together with the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification, proposes a unified nomenclature for Adhesion GPCRs. The new names have ADGR as common dominator followed by a letter and a number to denote each subfamily and subtype, respectively. The new names, with old and alternative names within parentheses, are: ADGRA1 (GPR123), ADGRA2 (GPR124), ADGRA3 (GPR125), ADGRB1 (BAI1), ADGRB2 (BAI2), ADGRB3 (BAI3), ADGRC1 (CELSR1), ADGRC2 (CELSR2), ADGRC3 (CELSR3), ADGRD1 (GPR133), ADGRD2 (GPR144), ADGRE1 (EMR1, F4/80), ADGRE2 (EMR2), ADGRE3 (EMR3), ADGRE4 (EMR4), ADGRE5 (CD97), ADGRF1 (GPR110), ADGRF2 (GPR111), ADGRF3 (GPR113), ADGRF4 (GPR115), ADGRF5 (GPR116, Ig-Hepta), ADGRG1 (GPR56), ADGRG2 (GPR64, HE6), ADGRG3 (GPR97), ADGRG4 (GPR112), ADGRG5 (GPR114), ADGRG6 (GPR126), ADGRG7 (GPR128), ADGRL1 (latrophilin-1, CIRL-1, CL1), ADGRL2 (latrophilin-2, CIRL-2, CL2), ADGRL3 (latrophilin-3, CIRL-3, CL3), ADGRL4 (ELTD1, ETL), and ADGRV1 (VLGR1, GPR98). This review covers all major biologic aspects of Adhesion GPCRs, including evolutionary origins, interaction partners, signaling, expression, physiologic functions, and therapeutic potential.
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3.
  • Homeyer, Andre, et al. (författare)
  • Automated quantification of steatosis: agreement with stereological point counting
  • 2017
  • Ingår i: Diagnostic Pathology. - : BIOMED CENTRAL LTD. - 1746-1596. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist. Methods: The evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability. Results: The new method showed the strongest agreement with the expert. At 20x resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10x resolution, it was more accurate than and twice as fast as all other methods at 20x resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer. Conclusions: The results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.
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