Qinke Peng

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-disease Association Prediction

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

BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction

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: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

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

LncDLSM: Identification of Long Non-coding RNAs with Deep Learning-based Sequence Model

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

CircRNA-disease inference using deep ensemble model based on triple association

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: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

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

SAILER: Scalable and Accurate Invariant Representation Learning for Single-Cell ATAC-Seq Processing and Integration

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

Predicting transcription factor binding in single cells through deep learning

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.