Juan Lavista Ferres

This paper applies parameter-efficient fine-tuning methods to proteomics, achieving competitive or better performance than traditional fine-tuning with significantly reduced computational resources.

This study identifies known pathogenic variants and new candidates in SUID using WGS, highlighting genetic factors related to heart disease and redox metabolism.

BindVAE is a deep learning method for discovering TF motifs from open chromatin data, achieving higher precision than existing methods but with lower recall.