Yu-Tian Wang
Orcid: 0000-0002-8033-8727
According to our database1,
Yu-Tian Wang
authored at least 17 papers
between 2019 and 2023.
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Bibliography
2023
scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.
IEEE J. Biomed. Health Informatics, December, 2023
Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
Convolution Neural Networks Using Deep Matrix Factorization for Predicting Circrna-Disease Association.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
2022
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAs.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA.
Appl. Artif. Intell., 2022
GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2022
Proceedings of the Intelligent Computing Theories and Application, 2022
A Unified Graph Attention Network Based Framework for Inferring circRNA-Disease Associations.
Proceedings of the Intelligent Computing Methodologies - 18th International Conference, 2022
2021
PLoS Comput. Biol., 2021
SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.
PLoS Comput. Biol., 2021
MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.
BMC Medical Informatics Decis. Mak., 2021
RWRNCP: Random Walking with Restart Based Network Consistency Projection for Predicting miRNA-Disease Association.
Proceedings of the Intelligent Computing Theories and Application, 2021
MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
Graph regularized L<sub>2, 1</sub>-nonnegative matrix factorization for miRNA-disease association prediction.
BMC Bioinform., 2020
2019
Proceedings of the Intelligent Computing Theories and Application, 2019