Minji Jeon

The paper uses an AI-driven framework to model perturbations in brain organoids and identifies candidate genes for autism using genomic and clinical data.

ArcDFI predicts drug-food interactions by incorporating drug-CYP450 interactions through attention regularization, improving generalizability and explainability.

ATOMIC predicts atopic dermatitis using a graph attention network incorporating microbial co-expression networks and genomic data from gut microbiome samples.

This study uses longitudinal multi-omic analyses of a mouse model to show seizure evolution in West syndrome involves synaptic remodeling, gliosis, and proteomic/lipidomic changes over time.

The paper describes the development of a metadata federation system for NIH Common Fund data to improve discoverability without data movement.

PrismExp improves gene function prediction by partitioning RNA-seq co-expression data into tissue-specific segments, outperforming cross-tissue approaches across multiple domains.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.