Suvo Chatterjee

TL;DR: MaAsLin 2 (Microbiome Multivariable Associations with Linear Models) as mentioned in this paper uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types with or without covariates and repeated measurements.

TL;DR: MaAsLin 2 (Microbiome Multivariable Associations with Linear Models) as mentioned in this paper uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types with or without covariates and repeated measurements.

TL;DR: In this article, a deep neural network-based artificial intelligence prediction model was proposed to identify a subgroup of patients undergoing carotid artery stenting who are at risk for short-term unplanned readmission.

TL;DR: In this article , the causal pathway from a genetic variant to birthweight by integrating placental methylation and gene expression with established GWAS loci for birthweight was investigated, which revealed candidate functional pathways that underpin the genetic regulation of birthweight via placental epigenetic and transcriptomic mechanisms.

TL;DR: In this paper, the authors identify and functionally characterize placental variably methylated regions (VMRs), which are regions in the epigenome with high inter-individual methylation variability, and investigate the contributions of fetal genetic loci and 12 prenatal environmental factors (maternal cardiometabolic-,psychosocial-, demographic and obstetric-related) on methylation at each VMR.