Lumen Chao

The paper introduces pertTF, a transformer-based model for predicting genetic perturbation responses, showing improved performance in expression prediction and cell identity shifts across cellular contexts.

The paper benchmarks multiple base editors and develops a model to predict editing efficiency, validating edits for disease-associated variants and identifying cancer-related SNVs.

The paper introduces a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

The paper develops DeepCas13, a deep learning model to predict CRISPR-Cas13d gRNA on-target activity and off-target viability effects, improving accuracy for RNA editing applications.

The paper evaluates CRISPR-Cas13 systems and finds that they have intrinsic RNA targets in mammalian cells, which can affect lentiviral particle production and limit their utility.