Fei Teng

The paper introduces eScreen, a deep learning model that decodes regulatory genome function at single-nucleotide resolution using CRISPR screen data, achieving high accuracy and mechanistic interpretation.

This study benchmarks five loss-of-function perturbation tools for studying noncoding genome function, finding dual-SpCas9 is most effective for targeting CREs and recommending RNA interference for noncoding RNA genes.

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.

This study identifies convergent therapeutic targets for chemoresistance in cancer using CRISPR screens across multiple drug conditions and cancer models.

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.

This paper introduces CECRID, a CRISPR-based detection system enhanced with L-proline to improve sensitivity and robustness for nucleic acid detection, particularly for SARS-CoV-2.