Research Areas

  • Omics Analysis: genomic analysis and variant calling; transcriptomic profiling and differential gene expression (DEG)

  • Statistical Analysis: Bayesian modeling and inference, uncertainty quantification

  • Application of Machine Learning in biomedical research

Journal Publications

1) La VNT et al. Linking biochemical and cellular efficacy of MERS coronavirus main protease inhibitors. Preprint. 2026

2) MacDermott-Opeskin H et al. A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data. J. Chem. Inf. Model. 2026; acs.jcim.5c02106.

3) La VNT, Kang L, Minh DDL, Enzyme kinetics model for the coronavirus main protease including dimerization and ligand binding. Biophysical Journal. 2025; 124:2627-2638

4) Boby ML et al. Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors. Science; 2023; 382:eabo7201.

5) La VNT, Minh DDL. Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation. International Journal of Molecular Sciences. 2023; 24:15074

6) La VNT, Nicholson S, Haneef A, Kang L, Minh DDL. Inclusion of Control Data in Fits to Concentration–Response Curves Improves Estimates of Half-Maximal Concentrations. Journal of Medicinal Chemistry. 2023; 66:12751–61

7) Nguyen TH, La VNT, Burke K, Minh DDL. Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures. PLOS ONE. 2022; 17:e0273656

8) La VNT, Tran HTD, Nguyen CH, Nguyen TTH. RNA-seq derived identification of coronatine-regulated genes putatively involved in terpenoid biosynthetic pathway in the rubber tree Hevea brasiliensis. IOP Conference Series: Earth and Environmental Science. 2021; 749:012033

Research Projects

1) Comparison of the three PPLs (numpyro, pyro and pymc3) for running the ITC (isothermal titration calorimetry) data

2) Copy Number Variant (CNV) Detection using Whole Exome Sequencing (WES)

3) Chromosomal Anomaly Identification using Whole Genome Sequencing (WGS)

4) Identifying breast cancer subtypes by unsupervised clustering techniques

5) Feature selection for cancer subtypes by supervised machine learning