Chan JKY
Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus
Keywords: Asian populations; digital health; gestational diabetes mellitus; HbA1c; machine learning; preconception care; prediction; preterm birth; public health; risk factors
ReadIntegrative multi-omics database (iMOMdb) of Asian pregnant women
Asians are underrepresented across many omics databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. As such, there is a pressing need for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in populations of Asian ancestry.
ReadIntegrative Multi-Omics database (iMOMdb) of Asian Pregnant Women.
Genome-wide association studies (GWAS) have helped identify associations between thousands of genetic variants with various diseases and traits (1). The molecular aetiologies of these phenotypes are further enhanced with molecular quantitative trait loci (QTL), linking molecular traits with phenotypes sharing genetic associations. In particular, genetic associations with gene expression and DNA methylation provide useful insight in understanding the linkage of susceptibility variants and their related genes and cell-specific regulatory elements (2).
ReadPlasmodium vivax binds host CD98hc (SLC3A2) to enter immature red blood cells
More than one-third of the world’s population is exposed to Plasmodium vivax malaria, mainly in Asia1. P. vivax preferentially invades reticulocytes (immature red blood cells)2,3,4. Previous work has identified 11 parasite proteins involved in reticulocyte invasion, including erythrocyte binding protein 2 (ref. 5) and the reticulocyte-binding proteins (PvRBPs)6,7,8,9,10.
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