Anna Paola Carrieri

This paper uses RNA foundation models to improve endometriosis classification across cohorts and identifies stable gene-level predictors.

This paper uses machine learning to predict circadian gene expression in Arabidopsis using DNA sequence features, identifies key regulatory elements, and optimizes sampling strategies for circadian transcript prediction.

This paper highlights challenges in benchmarking metagenomic profilers due to confusion between sequence abundance and taxonomic abundance.

EMPress provides an interactive tool for visualizing phylogenetic trees alongside multi-omic data, enabling exploratory analyses of microbiome and metabolome datasets.

The paper develops an explainable AI approach to predict skin-related phenotypes from leg skin microbiome data and identifies microbial composition changes linked to these traits.

The paper introduces a method for rapid microbiome analytics using streaming histogram sketches, enabling efficient classification and similarity searches of microbiome data.