Rafael S. Costa

On why and how to encode probability distributions on graph representations of omics data: enhancing predictive tasks and knowledge discovery

The paper introduces a graph-based framework that integrates statistical distributions into omics data to improve predictive performance and biological interpretability in cancer datasets.

Pattern-centric transformation of omics-data sources grounded on multi-wise gene associations aids predictive tasks in TCGA while ensuring interpretability.

The paper transforms omics data from gene-centric to pattern-centric to improve predictive performance and interpretability in TCGA data.

DISA tool: discriminative and informative subspace assessment with categorical and numerical outcomes

DISA assesses discriminative subspaces for numerical outcomes in omic data without discretization drawbacks, showing improved correlation gain.

A general deep hybrid model for bioreactor systems: combining first Principles equations with deep neural networks

This paper introduces deep hybrid models combining first principles equations with deep neural networks for bioreactor systems, showing improved generalization and reduced CPU time compared to shallow models.

Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2

This paper uses biclustering to analyze SARS-CoV-2 infection's transcriptional responses, finding more enriched GO terms and KEGG pathways than machine learning approaches.