Zixuan Wang
Orcid: 0000-0003-4829-1073
According to our database1,
Zixuan Wang
authored at least 16 papers
between 2021 and 2025.
Collaborative distances:
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Bibliography
2025
An overview of computational methods in single-cell transcriptomic cell type annotation.
Briefings Bioinform., 2025
Supervised pre-training for feature extraction in cell type annotation of single-cell multi-omics data.
Appl. Soft Comput., 2025
2024
scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis.
Comput. Biol. Medicine, 2024
Cell-Specific Highly Correlated Network for Self-Supervised Distillation in Cell Type Annotation.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024
2023
Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding.
Bioinform., October, 2023
HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.
Briefings Bioinform., September, 2023
HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency.
Bioinform., May, 2023
HGTDG: An Interpretable Heterogeneous Graph Transformer Framework for Cancer Driver Gene Prediction.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
KDProg: A Knowledge distillation graph neural network for cancer prognosis prediction and analysis.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023
2022
Comput. Biol. Medicine, 2022
A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape.
Briefings Bioinform., 2022
Predicting cell type-specific effects of variants on TF-DNA binding by meta-learning.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022
Single-cell TF-DNA binding prediction and analysis based on transfer learning framework.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022
2021
High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.
Briefings Bioinform., 2021
By hybrid neural networks for prediction and interpretation of transcription factor binding sites based on multi-omics.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021