Hoa Giang

This paper develops a cell-free DNA fragmentomic classifier using first-trimester NIPT data to predict spontaneous preterm birth with high accuracy.

This study investigates HBV cell-free DNA as a biomarker for viral infectivity in prenatal testing, finding it correlates with high infectivity and reduced fetal fraction in early pregnancy.

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.

This study analyzes NIPT data from 2,683 Vietnamese pregnant women to identify 8.2 million new SNPs and 24,487 disease-associated variants, highlighting the need for population-specific genetic studies.