Hugo Martiniano

The paper develops a text mining pipeline to create a ncRNA-phenotype relational corpus and evaluates a large language model for relation extraction, achieving high accuracy.

The paper identifies 70 miRNAs, including novel candidates, involved in ASD by analyzing genomic variants and their regulatory networks.

DGH-GO is an application that uses Gene Ontology to cluster disease-causing genes and identify genetic heterogeneity in complex diseases like ASD and NDDs.

This study uses machine learning to identify biological mechanisms underlying ASD subgroups based on clinical and genetic data, finding distinct processes in mild and severe cases.

This study identifies genes involved in detoxification and barrier permeability with variants linked to ASD and discovers gene-environment interactions relevant to the disorder.

This study uses machine learning with gene functional similarities to identify novel ASD genes, achieving higher accuracy than existing methods.