Juan Lavista Ferres

Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning

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

Known pathogenic gene variants and new candidates detected in Sudden Unexpected Infant Death using Whole Genome Sequencing

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

BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin

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