Laiyi Fu

S3RL improves spatial transcriptomics analysis by enhancing resolution, clustering, and ligand-receptor detection across multiple datasets.

KGRACDA predicts circRNA-disease associations by capturing local depth information in a knowledge graph, outperforming existing methods.

BertNDA predicts associations between miRNA, lncRNA, and diseases using a graph-Bert model with multi-scale information fusion, achieving better performance than existing methods.

KGETCDA predicts circRNA-disease associations using a knowledge graph encoder from Transformer, outperforming existing methods by capturing high-order interactions.

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

This paper proposes a deep ensemble model, DeepInteract, to predict circRNA-disease associations by integrating miRNA interactions and disease-related miRNA information, showing superior performance over existing methods.

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.

This paper introduces scFAN, a deep learning model that predicts genome-wide TF binding in single cells using single-cell ATAC-seq data and pre-trained on bulk ATAC-seq, DNA sequence, and ChIP-seq data.