Marc Pielies Avelli

The paper applies variational autoencoders to integrate biomedical data, explores model dynamics, and evaluates methods using synthetic and real datasets.

This study identifies peripheral immune and metabolic tissues as key contributors to Alzheimer's disease genetic risk, independent of brain-specific factors.

SLIM predicts single-cell genetic perturbation responses using STRING embeddings and a closed-form ridge regression model, achieving competitive accuracy with low computational cost.

The paper introduces CLASTER, a deep learning model that predicts nascent transcription levels from chromatin landscape and 3D structure data, achieving high correlation with experimental measurements.

PlasMAAG improves plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning, outperforming existing methods in accuracy and efficiency.

The paper applies a variational autoencoder to multi-omics data from the UK Biobank to assess disease risk prediction and the impact of preprocessing on model performance.