Helena Andrés-Terré

CellVGAE uses graph attention networks for scRNA-seq analysis, improving dimensionality reduction and clustering across multiple datasets.

REM extracts interpretable rules from deep learning and non-deep learning models to enhance explainability in healthcare decision-making.

This study introduces gGAN, an adversarial simulator of gene expression data that outperforms existing simulators in realism and passes a Turing test for gene expression data.

This paper develops a computational framework using variational autoencoders and graph autoencoders to model cell differentiation from single-cell RNA-seq data across species.

This paper explores autoencoder architectures for integrating cancer patient data and demonstrates their effectiveness in analyzing heterogeneous breast cancer data for accurate diagnosis.

GEESE uses a metabolic model to improve latent space learning for gene expression data, capturing biologically relevant features and enhancing generalization.