André Ricardo Gonçalves

Meta2DB provides a curated metagenomic database with 13,897 samples across 84 studies for predicting human health states using ML models.

This paper introduces an integer linear programming method for optimizing antibody libraries by balancing diversity and affinity, showing improved performance over baseline designs.

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.

AbBERT is a transformer-based language model trained on antibody sequences to predict humanness, showing utility in antibody design and optimization.