Ko-Han Lee

Predicting Neoantigen Immunogenicity from In Vivo Immune Editing

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes

NeoPrecis improves immunotherapy response prediction by integrating immunogenicity and clonality-aware neoantigen landscapes, showing better performance than tumor mutation burden in melanoma and NSCLC.

Trans-ancestry polygenic models for the prediction of LDL blood levels: An analysis of the UK Biobank and Taiwan Biobank

This paper compares ancestry-specific and multi-ancestry polygenic risk scores for LDL prediction in East Asian populations using UK Biobank and Taiwan Biobank data, finding that multi-ancestry models are more generalizable.

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

PGSbuilder provides a web platform for GWAS and PRS analysis, enabling medical professionals to assess genetic risk using variant annotations and multiple PRS methods.

Connecting MHC-I-binding motifs with HLA alleles via deep learning

This paper introduces MHCfovea, a deep learning framework that connects HLA alleles with binding motifs, revealing critical polymorphic residues for antigen discovery and vaccine design.

variant2literature: full text literature search for genetic variants

variant2literature searches biomedical literature for genetic variants by processing images and normalizing variant formats to improve recall.