Daniel Remondini

The paper estimates volatile metabolite levels in cheese using metatranscriptomic data and machine learning to link gene expression to biochemical pathways.

GeNETop improves context-specific metabolic models by integrating flux variability, network topology, and transcriptomics to enable dynamic simulations.

The paper uses machine learning to predict antigenic properties of influenza A(H3) virus from genomic data, finding that deep learning models outperform classical methods in fine-grained antigenic analysis.

This study uses sewage metagenomics to distinguish seasonal, human-derived, and environmental microbial communities, offering potential for improved AMR surveillance.

This study investigates biases in network reconstruction from metagenomic data using correlation measures and shows how compositional data transformations can improve accuracy.

This study analyzes network reconstruction biases in metagenomic data using correlation measures and shows how compositional data transformations improve accuracy.

DNetPRO identifies low-dimensional signatures from high-throughput data by combining discriminant analysis with network processing, outperforming existing methods with fewer variables.

The paper identifies three tumor-specific gene signatures through network analysis of multi-tumor omics data and validates them with in vitro experiments to suggest novel drug targets and combinations.

This study quantifies how four resistance mechanisms contribute to ciprofloxacin minimum inhibitory concentration in E. coli using a systematic review of 66 studies.