Rana A. Barghout
PhD
Co-advised with Krishna Mahadevan
Publications within the group (3)
Do protein representations encode variant function? Diagnosing residue-level protein attributions via embedding-space gradients / bioRxiv / '26
Introduces IGLOO, a diagnostic framework that evaluates whether residue-level embedding gradients from protein language models faithfully capture functional importance against synthetic and experimental fitness landscapes.
Rana A. Barghout, Shengqing Wu, William Chau, José Manuel Barraza-Chavez, Paola Driza, Radhakrishnan Mahadevan, Benjamin Sanchez-Lengeling
kinGEMs: A scalable framework for resource-constrained models through stochastic tuning of deep learning-predicted kinetic parameters / PLOS Comput Biol / '26
Couples deep learning turnover number predictions with stochastic simulated annealing to parameterize enzyme-constrained genome-scale models across 93 organisms, resolving long-standing biocatalytic data sparsity.
Graph Data Modeling: Molecules, Proteins, & Chemical Processes / ACS In Focus / '25
A foundational pedagogical text and monograph introducing structural, geometric, and kinetic graph neural networks for molecules, macromolecular proteins, and complex biological pathways.
José Manuel Barraza-Chavez, Rana A. Barghout, Ricardo Almada-Monter, Adrian Jinich, Radhakrishnan Mahadevan, Benjamin Sanchez-Lengeling