Vinh Sy Le

An efficient deep learning method for amino acid substitution model selection

The paper introduces a deep learning method, ModelDetector, for selecting amino acid substitution models, achieving comparable accuracy to maximum likelihood methods but with significantly faster computation.

AMRomics: a scalable workflow to analyze large microbial genome collection

AMRomics is a scalable pipeline for analyzing large microbial genome collections, showing better performance than existing tools.

An Efficient Computational Method to Create Positive NIPT Samples with Autosomal Trisomy

The paper introduces a computational method to generate positive NIPT samples with autosomal trisomy and evaluates two algorithms on these samples, showing improved sensitivity and specificity.

Estimating amino acid substitution models from genome datasets: A simulation study on the performance of estimated models

This study simulates genome datasets to evaluate how well maximum likelihood methods estimate amino acid substitution models and their performance in building ML trees.

nQMaker: estimating time non-reversible amino acid substitution models

nQMaker estimates time non-reversible amino acid substitution models and rooted phylogenetic trees from protein sequence alignments, showing improved model fit across diverse datasets.