Hyun Uk Kim

BayesTx identifies causal cancer drug targets by integrating patient and cell line data using Bayesian networks, ranking 47 transcription factors and validating them with survival and drug response data.

This study analyzes 1432 transcriptomics datasets from 132 Actinomycetota strains to identify patterns of BGC expression and regulatory mechanisms.

This paper develops a computational framework using dimensionality reduction of genome-scale metabolic models to identify host metabolic vulnerabilities for antiviral targets, validated across multiple coronaviruses.

This study performs multi-omics analysis and builds a genome-scale metabolic model for Bos taurus muscle cells to improve cultured meat production.

This study develops machine learning models using EHR data to predict physiological effects of multiple drugs, achieving an average AUROC of 0.75.

This paper introduces a machine learning framework that extracts biological pathway information from literature images and compares it to manually curated maps.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

The paper develops a computational workflow to predict metabolite-gene-pathway associations in cancers using genome-scale metabolic models and mutation data, showing significant associations with specific somatic mutations.

The paper identifies new cancer predisposing genes by analyzing rare germline variants in Mendelian disease genes across cancer cohorts.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.