Yuheng Du

HOMED improves DNA methylation deconvolution by integrating cell-lineage hierarchies and single-cell data, achieving higher accuracy across tissues.

STDrug improves drug repurposing by integrating spatial transcriptomics with machine learning to prioritize patient-specific therapies.

Originator identifies and removes blood contamination in scRNA-seq data using genetic and contextual information, improving accuracy in downstream analysis.

The paper uses single-cell imaging features to predict breast cancer patient survival and identifies seven survival subtypes with distinct cellular profiles.

This study identifies and validates two molecular subtypes within early and late mild cognitive impairment stages of Alzheimer's disease using integrated metabolomics and transcriptomics data.

Lilikoi V2.0 is a new R package that uses deep learning and machine learning for disease classification and prognosis prediction using metabolomics data.

The paper evaluates ten cell-type annotation methods on scRNA-seq data, finding Seurat performs best for major cell types but struggles with rare populations and similar cell types.