Kun Hee Kim

This paper introduces TCR-FramePose, a geometric framework for analyzing TCR-pMHC recognition, showing it captures novel structural features beyond conventional descriptors.

The paper constructs immune-specific gene sets using non-negative matrix factorization on bulk RNA-seq data to improve immune subtype analysis and ICB response prediction.

The paper introduces epiNB, an interpretable machine learning model for predicting patient-specific HLA-I antigen presentation using Mass Spectrometry data, achieving state-of-the-art accuracy with fewer parameters and no hyperparameter tuning.

novoRNABreak detects novel splice junctions and fusion transcripts in cancer RNA-seq data with high sensitivity and efficiency.