Mehdi M. Momen

This study uses data-driven exploratory factor analysis and Bayesian networks to identify latent phenotypes and their genetic relationships in wheat, revealing how traits like yield and disease resistance are interrelated.

The paper predicts biological age using DNA methylation data with RKHS and Bayesian ridge regression, finding higher accuracy with unadjusted methylation data and sex-specific aging patterns.

This study uses regional heritability mapping to identify genomic regions associated with agronomic traits in sugarcane, finding high-heritability windows linked to fiber, sucrose, and yield traits.

The paper applies multi-trait random regression models to predict daily water usage in rice using high-throughput phenotyping data, showing improved genomic prediction accuracy when integrating shoot biomass measurements.

The paper introduces a genomic structural equation model to examine relationships between phenotypic plasticity and local environments using Bayesian networks and mixed-effects SEM.

This paper introduces SEM-GWAS, a method that incorporates phenotypic networks into GWAS to partition SNP effects into direct and indirect effects, using rice traits as an example.

This study applies random regression models with genomic Legendre polynomials and B-splines to predict rice shoot area under control and water-limited conditions, showing improved prediction accuracy compared to a baseline model.

This paper uses random regression models to improve genetic inference of longitudinal traits in rice by identifying both persistent and time-specific quantitative trait loci.

The paper applies structural equation models to GWAS in chickens to decompose SNP effects into direct, indirect, and total effects, showing greater insight into causal relationships than traditional multi-trait methods.