Heewook Lee

RulePep is a neural-symbolic classifier for peptide-function prediction that combines ESM-2 representations with interpretable rules to achieve high performance on three peptide classification tasks.

This paper develops an AI-driven framework to predict and explain miRNA strand selection across species, revealing conserved rules governing this process.

This study enhances T cell receptor generation for novel epitopes using in-context learning and self-contemplation prompting, achieving performance comparable to or better than ground-truth TCRs.

The paper introduces context-aware amino acid embeddings for TCR sequences, showing improved performance in binding affinity prediction and clustering compared to existing methods.

This study analyzes mutations in E. coli with mismatch repair defects and finds no significant orientation bias in mutation frequencies between genes oriented co-directionally or head-on to replication.

This study uses mutation accumulation and whole-genome sequencing to reveal new complexities in SOS-induced untargeted mutagenesis in Escherichia coli, showing sequence and DNA-strand bias in transversion mutations.

This study identifies microbiota-dependent gene expression changes in Drosophila melanogaster heads using RNA-seq and nanoString nCounter, revealing genes involved in immune response, metabolism, and stress resistance.

This study uses whole-genome sequencing of MMR-defective E. coli to identify base-pair substitution patterns and their determinants, revealing context-dependent mutational hotspots and strand bias.

This paper introduces a graph-guided assembly method for HLA typing that enables accurate identification of both known and novel alleles from whole genome sequencing data.