Astrid Moura Vicente

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

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.

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