Guiomar Oliveira

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

EAGER establishes a registry of autistic individuals and those with rare genetic conditions for genomic and phenotypic research, aiming to improve clinical trials and health outcomes through data sharing.

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 paper pilots the adaptation of the ELEAT tool in Portugal to assess environmental exposures in children with ASD, aiming to identify modifiable risk factors for the disorder.

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