Pablo Meyer

The paper presents an ensemble model that improves predictions of perceptual similarity between odor mixtures using a curated dataset and collaborative challenge results.

The paper predicts end-stage renal disease from asymptomatic individuals using multimodal data and identifies novel genomic biomarkers associated with kidney function and disease risk.

This paper shows that age information is essential for Alzheimer's Disease detection using brain MRI, outperforming age-invariant methods and revealing flaws in brain-age gap as a biomarker.

GENET streamlines hypothesis generation for gene-disease associations by integrating AI-driven network analysis, literature mining, clustering, and visualization.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

The paper uses semantic descriptors from language to predict odor mixture discriminability, outperforming existing methods by leveraging human perception data.

The paper predicts cellular positions in the Drosophila embryo using scRNA-seq data by benchmarking methods against spatially annotated genes.

The paper predicts perceptual ratings for odorants using natural language semantics and chemoinformatic features, achieving higher accuracy for more descriptors than previous methods.

The paper shows that variable mitochondria abundance affects cell survival and fractional cell death response to TRAIL through Bax effective concentration variation.

The paper demonstrates that machine learning can accurately predict odor perception from molecular features, enabling reverse-engineering of smell.