Camilo Valdes

The paper introduces an embeddings fusion approach to predict disease state from microbiome features using visual representations of taxonomic data, achieving high classification accuracy across multiple factors.

This study integrates host and sample metadata with microbiome abundance profiles to improve disease prediction models using machine learning.

The paper introduces a visualization tool for metagenomic data using Hilbert curves to create interactive 2D microbiome maps that highlight microbial abundance patterns.

The paper introduces a method to analyze microbiome antibiotic response using replication rates and causal inference, finding microbes reduce replication under beta-lactams and specific resistance genes enable dominance.