Yanming Li

The paper introduces a supervised learning algorithm for multi-phenotype genomic prediction, showing improved performance in gene expression and DNA methylation analysis and cell type deconvolution.

PerSEveML identifies persistent biomarker structures for rare events using an integrative machine learning approach and provides visualization tools for hypothesis generation.

This study identifies corticocortical connections of the rostral forelimb area in rats, showing similarities to primate ventral premotor cortex connections.

The paper introduces a machine learning method to detect brain connectomic biomarkers differentiating Alzheimer's disease and mild cognitive impairment using FDG-PET data.

This paper introduces a gene-level methylome-wide association analysis method called CMO that identifies novel Alzheimer's disease genes by integrating DNA methylation and enhancer-promoter interactions with GWAS data.