Daniel Ruiz-Perez

The paper introduces METALICA, a tool for causal inference in microbiome data, to uncover interactions and confounders in longitudinal multi-omic datasets.

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 computational pipeline, PALM, that integrates multi-omics time-series microbiome data using dynamic Bayesian networks to identify microbial and host interactions.

The paper compares PLS-DA and PCA for feature selection using synthetic and microbiome data, showing PCA can outperform PLS-DA in some cases.

This paper introduces a signed Bayesian Network (sBN) to infer directional relationships in microbial communities using abundance data, finding that directed edges correlate with colonization order.