Joung Min Choi

ARGfore forecasts antibiotic resistance gene abundances from time-series metagenomic data, outperforming standard methods with lower error and better efficiency.

CancerSubminer improves cancer subtyping by combining supervised and unsupervised learning on DNA methylation data, identifying prognostic subtypes across multiple cancers.

This study uses machine learning to predict FSHD using multi-omics data and identifies key biomarkers, showing improved accuracy with limited samples.

CIWARS is a web server for analyzing longitudinal metagenomic data to detect antibiotic resistance genes and assess AR risks in environmental samples.