Tiago Azevedo

This paper uses deep learning object detection models to automate tau lesion identification in PSP brain slides, incorporating uncertainty quantification to improve reliability and clinical relevance.

This paper introduces a deep graph neural network combining GNNs and TCNs to model spatio-temporal dynamics in rs-fMRI data, showing explainable neurobiological insights.

The paper analyzes the human transcriptome using co-expression networks and identifies biologically meaningful clusters and associations with CRP and cancer metastasis.

This paper introduces GAIN-GTEx, a gene expression imputation method that outperforms existing approaches on RNA-Seq data from cancer types and diverse tissues.

This paper predicts brain age using graph neural networks integrated with multimodal data, showing robustness to noise and graph sparsity.

This paper develops a machine learning tool to interpret cognitive differences using brain features from neuroimaging data.