Health, Environment, Cognitive Aging

The paper identifies distinct brain maturation pathways linking environmental variation to cognitive development in adolescents using longitudinal data.

The paper introduces a machine learning framework, ISI, to infer how organisms prioritize environmental signals from time-series data, applied to medaka fish gonadal development under natural conditions, revealing dynamic signal importance and gene expression correlations.

DeepEXPOKE uses deep learning with knockoff features to distinguish genetic and non-genetic exposure risks in sepsis and coronary heart disease, identifying metabolites and asthma as risk factors.

This study applies longitudinal exposomics to a multiomic cohort, revealing dynamic environmental chemical mixtures and their associations with biological markers over time.

ExposomeX is a platform that standardizes exposomic analysis to explore the 'Exposure-Biology-Disease' nexus through six modules and validated with multi-omics studies.

This study integrates longitudinal personal exposome and multi-omics data to show dynamic interactions between environmental exposures and internal biomolecules, highlighting key agrochemicals and fungi.

The paper integrates multiomics and digital health data from 96 individuals over 16 months to identify independent molecular factors linked to health outcomes and lifestyle changes.

The paper introduces a linear mixed model integrating genomic and exposomic data to improve phenotypic prediction for complex traits, showing increased variance explanation and prediction accuracy.