Jiyuan Hu

The paper introduces JM-NCC, a joint modeling framework for nested case-control studies with competing risks, applied to the TEDDY microbiome dataset to analyze biomarker and survival outcomes.

The paper introduces GIF, a framework to disentangle direct and indirect genotype-phenotype associations using imaging data, showing image features mediate most associations in ADNI data.

LongStrain improves strain-level analysis in longitudinal metagenomic data by jointly modeling strain proportions and genomic variants, outperforming existing methods in variant calling and proportion estimation.

LIMBARE improves breakpoint estimation in longitudinal ophthalmic studies by using robust linear mixed-effects modeling, outperforming other methods in simulations and real data.

The paper introduces a new statistical test for detecting differential abundance in high-dimensional microbiome data, showing improved power and control of type I error compared to existing methods.

ARZIMM infers microbial interactions and community stability from longitudinal microbiome data using a zero-inflated mixed-effects model.

The paper introduces a microbial trend analysis framework to characterize dynamic microbial patterns in longitudinal studies, showing its effectiveness in trend detection, group comparison, and classification.

The paper introduces JointMM, a joint modeling framework for analyzing longitudinal microbiome proportions and time-to-event data, showing its effectiveness in identifying microbial associations with disease progression.

The paper introduces a causal mediation model for high-dimensional microbiome data to estimate and test microbial causal effects in a three-factor study design.