Daniel Antonio Negrón

The paper develops a machine learning model to improve LAMP assay design by identifying key thermodynamic features from primer sets, achieving high classification accuracy.

The paper develops an algorithm for designing LAMP assays and a computer vision tool for analyzing colorimetric outputs to detect antimicrobial resistance in Vibrio cholerae.

The paper presents clade-specific PCR assays for mpox virus detection, improving accuracy and enabling clade identification during outbreaks.

Capsid.js is a browser-based tool that generates high-quality SVG images of class I viral capsids with customizable styles and perspectives.

BioLaboro detects molecular assay signature erosion and designs new assays for emerging pathogens using in silico analysis of genome sequences.