A Pipeline for Data-Driven Learning of Topological Features with
Applications to Protein Stability Prediction
A Pipeline for Data-Driven Learning of Topological Features with
Applications to Protein Stability Prediction
In this paper, we propose a data-driven method to learn interpretable topological features of biomolecular data and demonstrate the efficacy of parsimonious models trained on topological features in predicting the stability of synthetic mini proteins. We compare models that leverage automatically-learned structural features against models trained on a large set …