Marc Pagès-Gallego

The paper develops a method to directly detect 8-oxo-dG using nanopore sequencing by creating synthetic DNA with context variability and training deep learning models.

This paper introduces NanoRCS, a method using nanopore sequencing and consensus calling to accurately estimate tumor fractions from cfDNA by analyzing SNVs, CNAs, and fragmentomics.

This paper develops a deep learning model for intraoperative pediatric CNS tumor classification using rapid sequencing, achieving accurate results within 60-90 minutes.

The paper benchmarks and analyzes deep learning models for Nanopore sequencing basecalling, finding that Bonito has the best architecture and that species bias affects performance.