GRRFNet: Guided Regularized Random Forest-based Gene Regulatory Network Inference Using Data Integration. - Yongqing Zhang 001; Qingyuan Chen; Dongrui Gao; Quan Zou 001 (BIBM, 2020)

A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning. - Yuanqi Zeng; Meiqin Gong; Meng Lin; Dongrui Gao; Yongqing Zhang 001 (IEEE Access, 2020)

By hybrid neural networks for prediction and interpretation of transcription factor binding sites based on multi-omics. - Yongqing Zhang 001; Zixuan Wang 025; Yuhang Liu; Libo Lu; Xiaoyao Tan; Quan Zou 001 (BIBM, 2021)

High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method. - Yongqing Zhang 001; Zixuan Wang 025; Yuanqi Zeng; Jiliu Zhou; Quan Zou 001 (Briefings Bioinform., 2021)

MFFNet: Multi-dimensional Feature Fusion Network based on attention mechanism for sEMG analysis to detect muscle fatigue. - Yongqing Zhang 001; Siyu Chen; Wenpeng Cao; Peng Guo; Dongrui Gao; Manqing Wang; Jiliu Zhou; Ting Wang (Expert Syst. Appl., 2021)

Single-cell TF-DNA binding prediction and analysis based on transfer learning framework. - Zixuan Wang 025; Yongqing Zhang 001; Yun Yu; Maocheng Wang; Yuhang Liu; Quan Zou 001 (BIBM, 2022)

Predicting cell type-specific effects of variants on TF-DNA binding by meta-learning. - Yongqing Zhang 001; Yuhang Liu; Zixuan Wang 025; Maocheng Wang; Shuwen Xiong; Quan Zou 001 (BIBM, 2022)

A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape. - Yongqing Zhang 001; Zixuan Wang 025; Yuanqi Zeng; Yuhang Liu; Shuwen Xiong; Maocheng Wang; Jiliu Zhou; Quan Zou 001 (Briefings Bioinform., 2022)

A survey on the algorithm and development of multiple sequence alignment. - Yongqing Zhang 001; Qiang Zhang; Jiliu Zhou; Quan Zou 001 (Briefings Bioinform., 2022)

Multiple Sequence Alignment based on deep Q network with negative feedback policy. - Yongqing Zhang 001; Qiang Zhang; Yuhang Liu; Meng Lin; Chunli Ding (Comput. Biol. Chem., 2022)

Towards a better understanding of TF-DNA binding prediction from genomic features. - Zixuan Wang 025; Meiqin Gong; Yuhang Liu; Shuwen Xiong; Maocheng Wang; Jiliu Zhou; Yongqing Zhang 001 (Comput. Biol. Medicine, 2022)

Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation. - Yuhang Liu; Tianhao Li; Zixuan Wang 025; Guiquan Zhu; Yongqing Zhang 001; Quan Zou 001 (BIBM, 2023)

KDProg: A Knowledge distillation graph neural network for cancer prognosis prediction and analysis. - Shuwen Xiong; Zixuan Wang 025; Junming Zhang; Hong Luo; Yongqing Zhang 001; Quan Zou 001 (BIBM, 2023)

HGTDG: An Interpretable Heterogeneous Graph Transformer Framework for Cancer Driver Gene Prediction. - Shuwen Xiong; Zixuan Wang 025; Junming Zhang; Guiquan Zhu; Yongqing Zhang 001; Quan Zou 001 (BIBM, 2023)

SHNN: A single-channel EEG sleep staging model based on semi-supervised learning. - Yongqing Zhang 001; Wenpeng Cao; Lixiao Feng; Manqing Wang; Tianyu Geng; Jiliu Zhou; Dongrui Gao (Expert Syst. Appl., 2023)

HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency. - Zixuan Wang 025; Shuwen Xiong; Yun Yu; Jiliu Zhou; Yongqing Zhang 001 (Bioinform., 2023)

T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention network. - Bo Peng; Yongqing Zhang 001; Manqing Wang; Jun Chen; Dongrui Gao (Comput. Biol. Chem., 2023)

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction. - Yuhang Liu; Zixuan Wang 025; Hao Yuan; Guiquan Zhu; Yongqing Zhang 001 (Briefings Bioinform., 2023)

An EEG-based Brain Cognitive Dynamic Recognition Network for representations of brain fatigue. - Pengrui Li; Yongqing Zhang 001; Shihong Liu; Liqi Lin; Haokai Zhang; Tian Tang; Dongrui Gao (Appl. Soft Comput., 2023)

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding. - Yuhang Liu; Hao Yuan; Qiang Zhang; Zixuan Wang 025; Shuwen Xiong; Naifeng Wen; Yongqing Zhang 001 (Bioinform., 2023)

Cell-Specific Highly Correlated Network for Self-Supervised Distillation in Cell Type Annotation. - Tianhao Li; Yugui Xu; Sihan He; Yuhang Liu; Zixuan Wang 025; Zhigan Zhou; Yongqing Zhang 001; Quan Zou 001 (BIBM, 2024)

scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis. - Meiqin Gong; Yun Yu; Zixuan Wang 025; Junming Zhang; Xiongyi Wang; Cheng Fu; Yongqing Zhang 001; Xiaodong Wang (Comput. Biol. Medicine, 2024)

A multiscale feature fusion network based on attention mechanism for motor imagery EEG decoding. - Dongrui Gao; Wen Yang; Pengrui Li; Shihong Liu; Tiejun Liu; Manqing Wang; Yongqing Zhang 001 (Appl. Soft Comput., 2024)

An EEG-based cross-subject interpretable CNN for game player expertise level classification. - Liqi Lin; Pengrui Li; Qinghua Wang; Binnan Bai; Ruifang Cui; Zhenxia Yu; Dongrui Gao; Yongqing Zhang 001 (Expert Syst. Appl., 2024)

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection. - Dongrui Gao; Pengrui Li; Manqing Wang; Yujie Liang; Shihong Liu; Jiliu Zhou; Lutao Wang; Yongqing Zhang 001 (IEEE J. Biomed. Health Informatics, 2024)

SFT-Net: A Network for Detecting Fatigue From EEG Signals by Combining 4D Feature Flow and Attention Mechanism. - Dongrui Gao; Kejie Wang; Manqing Wang; Jiliu Zhou; Yongqing Zhang 001 (IEEE J. Biomed. Health Informatics, 2024)

Supervised pre-training for feature extraction in cell type annotation of single-cell multi-omics data. - Yongqing Zhang 001; Tianhao Li; Yuhang Liu; Hong Luo; Yugui Xu; Zixuan Wang 025; Quan Zou 001 (Appl. Soft Comput., 2025)

An overview of computational methods in single-cell transcriptomic cell type annotation. - Tianhao Li; Zixuan Wang 025; Yuhang Liu; Sihan He; Quan Zou 001; Yongqing Zhang 001 (Briefings Bioinform., 2025)

MMGCSyn: Explainable synergistic drug combination prediction based on multimodal fusion. - Yongqing Zhang 001; Hao Yuan; Yuhang Liu; Shuwen Xiong; Zhigan Zhou; Yugui Xu; Xinyu Mao; Meiqin Gong (Future Gener. Comput. Syst., 2025)

A Comprehensive Adaptive Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Fatigue Driving Detection. - Dongrui Gao; Shihong Liu; Yingxian Gao; Pengrui Li; Haokai Zhang; Manqing Wang; Yan Shen 001; Lutao Wang; Yongqing Zhang 001 (IEEE Trans. Fuzzy Syst., 2025)

Prediction of cancer drug response based on heterogeneous graph neural networks and multi-omics data. - Junming Zhang; Shuwen Xiong; Yugui Xu; Yongqing Zhang 001 (Neural Networks, 2026)