Xinfei Wang
Orcid: 0000-0003-0554-2936Affiliations:
- Jilin University, College of Computer Science and Technology, Changchun, China
According to our database1,
Xinfei Wang authored at least 15 papers
between 2023 and 2026.
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Bibliography
2026
A Dynamic Multi-Scale Hypergraph Learning Framework Driven by Features and Structures for ceRNA-Disease Association Prediction.
IEEE J. Biomed. Health Informatics, March, 2026
MuGNet-CMI: Multi-Head Hybrid Graph Neural Network for Predicting circRNA-miRNA Interactions With Global High-Order and Local Low-Order Information.
IEEE Trans. Big Data, February, 2026
HpMiX: A Disease ceRNA biomarker prediction framework driven by graph topology-constrained Mixup and hypergraph residual enhancement.
Neural Networks, 2026
2025
Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks.
J. Chem. Inf. Model., 2025
Hither-CMI: Prediction of circRNA-miRNA Interactions Based on a Hybrid Multimodal Network and Higher-Order Neighborhood Information via a Graph Convolutional Network.
J. Chem. Inf. Model., 2025
MuseCDA: Predicting CircRNA-Disease Associations Via Multi-Scale Structure Embedding.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2025
GSAM-MRI: Frequency-Based Domain Randomization for Generalized MR Image Segmentation with Segment Anything Model.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2025
2024
BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.
BMC Bioinform., December, 2024
MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.
BMC Bioinform., December, 2024
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.
Briefings Bioinform., November, 2024
Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.
Briefings Bioinform., November, 2024
A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer.
Briefings Bioinform., November, 2024
RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.
J. Chem. Inf. Model., 2024
2023
An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.
Comput. Biol. Medicine, October, 2023
A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.
Briefings Bioinform., May, 2023