Dinara R. Usmanova

The paper uses neural networks to predict protein evolutionary rates by integrating molecular and cellular features across multiple scales, showing that protein-level and site-level properties contribute additively to evolutionary rate variance.

The paper investigates the generation time effect on protein substitution rates across mammals and develops an integrated model of protein evolution.

The paper identifies a percolation-like transition in bacterial metabolic networks that correlates with lifestyle differences between obligate symbionts and free-living generalists.

The paper shows that protein evolution rates are inversely related to functional optimality and expression levels across multiple organisms and tissues.