Byung-Jun Yoon

This paper introduces PULSE, a context-aware steering method for protein design that improves optimization outcomes by considering sequence-specific directions, and introduces LDS to evaluate trajectory reliability.

CARTnet improves B-cell epitope prediction using a contact-aware deep learning framework, outperforming existing methods on internal and external datasets.

UPLiFT improves parameter efficiency in fine-tuning protein language models by optimizing activation space instead of network weights, achieving competitive performance with fewer trainable parameters.

This work evaluates factors affecting generalization of GNN-based TCR-pMHC binding predictors to unseen peptides and proposes architectural improvements.

This paper develops a spatio-temporal analysis framework to uncover coupled mutagenic mechanisms in radiation-induced genomic instability using whole-genome sequencing data from irradiated human cells.

This paper introduces a computational pipeline for antigen discovery that uses filtering and machine learning to identify conserved neutralizing epitopes in viruses like SARS-CoV-2 and RVFV.

The paper develops a sequence-based method to identify structure-disrupting mutations using protein language models, reducing the need for expensive structure prediction.

The paper models how low-dose radiation affects nuclear morphology by linking RNA-seq data to cell-painting imaging features over time using an interpretable inverse modeling framework.

The paper introduces SCALE, a pipeline for longitudinal cell painting analysis, demonstrating its ability to handle imaging variability and enable consistent comparisons across time points.

The paper introduces EXPORT, a VAE framework for analyzing ordinally perturbed transcriptomics data, enhancing interpretability by incorporating pathway knowledge and ordinal constraints.

The paper develops a sensitivity analysis strategy for genome-scale metabolic flux prediction using TRIMER, showing its utility in guiding optimal experimental design for metabolic engineering.

The paper develops an ODE model of PARP1 inhibition, identifies key parameters through sensitivity analysis, and discovers GSPT2 as a biomarker linked to cancer patient outcomes.

TRIMER integrates transcriptional and metabolic regulation to predict metabolic fluxes and gene states, outperforming existing methods on simulated and experimental data.

This paper develops a wireless platform combining AI for high-throughput behavioral analysis with dual-channel optogenetics to study brain dynamics in freely behaving rats.