Yingxin Cao

This paper uses AI-guided ADAR editing to correct a nonsense mutation in mice, improving Rett syndrome symptoms and extending lifespan.

Helix predicts A-to-I RNA editing by ADARs with high accuracy using a structure-aware transformer model, enabling efficient gRNA design for therapeutic applications.

This paper develops DeepREAD, a generative model for designing ADAR guide RNAs to enable efficient and specific RNA editing for therapeutic applications.

This paper introduces lncDLSM, a deep learning framework for identifying long non-coding RNAs without prior biological knowledge, showing effectiveness across species.

UFold predicts RNA secondary structures using deep learning, outperforming thermodynamic models and other learning-based methods with high accuracy and speed.

SAILER improves scATAC-seq analysis by learning invariant representations, enhancing clustering and imputation while scaling to large datasets.