Liang Liang

The paper presents a longitudinal transcriptomic atlas of maternal peripheral blood during healthy pregnancy, identifying 720 pregnancy-specific genes and nine transcriptional modules associated with immune and metabolic changes.

The paper introduces microbiomeMASST, a metadata network graph linking metabolites across datasets, enabling cross-study analysis of microbial metabolites and their biological context.

The paper studies how mouse thalamic local interneurons process visual stimuli by coordinating local and global dendritic responses to perform motion processing.

The paper investigates how excitatory inputs from retinal and non-retinal sources coordinate to shape motion selectivity in the mouse visual thalamus.

This study analyzes longitudinal vaginal microbiome data from diverse ethnic groups, revealing ethnic signatures in microbial diversity, community states, and evolutionary dynamics, with Lactobacillus adhesins showing positive selection.

The paper introduces deepPseudoMSI, a deep learning framework that converts LC-MS data into pseudo-MS images for improved disease diagnosis in precision medicine.

The paper uses longitudinal urine metabolomics to predict gestational age with a machine-learning model, showing non-invasive pregnancy dating potential.

metID automates compound annotation for LC-MS data by integrating multiple databases, improving efficiency and reproducibility.