Valentina Rukins
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View article: Multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycaemic traits during pregnancy reveals limited evidence of pregnancy-specific genetic effects
Multi-ancestry, trans-generational GWAS meta-analysis of gestational diabetes and glycaemic traits during pregnancy reveals limited evidence of pregnancy-specific genetic effects Open
Gestational diabetes mellitus (GDM) affects ∼14% of pregnancies and is linked to adverse pregnancy outcomes and increased maternal type 2 diabetes mellitus (T2DM) risk. The GenDiP Consortium conducted trans-generational, multi-ancestry gen…
View article: Interaction between genetic risk and comorbid conditions in endometriosis
Interaction between genetic risk and comorbid conditions in endometriosis Open
Endometriosis is a complex disease, and many genetic and environmental risk factors contribute to disease risk. The genetic risk of endometriosis has been well characterized in genome-wide association studies. While few physiological risk …
View article: Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY
Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY Open
View article: Large-scale genome-wide association study to determine the genetic underpinnings of female genital tract polyps
Large-scale genome-wide association study to determine the genetic underpinnings of female genital tract polyps Open
STUDY QUESTION Can a large-scale genome-wide association study (GWAS) meta-analysis identify the genomic risk loci and associated candidate genes for female genital tract (FGT) polyps, provide insights into the mechanism underlying their d…
View article: HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images Open
These two zipped folders contain all data necessary to train, validate and reproduce results from the paper. Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folde…
View article: HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images Open
These two zipped folders contain all data necessary to train, validate and reproduce results from the paper. Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folde…
View article: HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images Open
Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta’s heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the ‘Histol…