Hoai‐Nghia Nguyen

CALFP-MHC improves peptide-MHC binding prediction by using chemically grounded fingerprints and contrastive learning, achieving high AUC and PPV across diverse alleles and class imbalance.

This paper uses supervised non-negative matrix factorization on cfDNA fragmentomic features to improve early cancer detection, achieving high AUC scores in both pan-cancer and HCC-specific classifications.

This paper develops a machine learning model that integrates TCRβ and pHLA binding data to improve neoantigen prioritization in colorectal cancer, showing better performance than existing tools.

The paper develops a model combining tumor-specific methylation atlas and cfDNA features to improve tissue-of-origin detection in low-depth cfDNA samples.

This paper validates a comprehensive genomic profiling assay, K-4CARE, for detecting genetic variants and ctDNA, showing high sensitivity and specificity across multiple cancer types.

epiTCR-KDA improves TCR-peptide binding prediction by incorporating structural dihedral angles and knowledge distillation, achieving high AUC for both seen and unseen data.

This study identifies 85 recessive diseases in the Vietnamese population through exome sequencing, highlighting three highly prevalent conditions and discovering novel pathogenic variants.