Filip Buric

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

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.