Filip Buric

This paper shows that thermal adaptation can be transferred between unrelated enzymes using a generative framework, improving thermostability without evolutionary homology.

This study shows that amino acid sequences determine protein abundance by affecting conformational stability and synthesis cost, using deep learning and yeast experiments.

The paper trains a deep learning model to predict enzyme thermal adaptation using a dataset of enzymes labeled with optimal growth temperatures.

metaGEM reconstructs genome-scale metabolic models directly from metagenomes, demonstrating comparable metabolic representation to reference-based models and capturing intraspecies diversity in gut microbiomes.

The paper uses deep learning to show that gene expression levels are encoded in both coding and non-coding regions of the gene regulatory structure across multiple organisms.

PARADIAS deconvolves DIA spectra to improve quantification and identification of proteins by analyzing more ion data than traditional methods.