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Jiancheng Ni

Showing results (1-10 of 8) with videos related to

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Frontiers in Genetics|May 1, 2020
MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease AssociationQingwen Wu, Yutian Wang, Zhen Gao, et al.
International Journal of Molecular Sciences|August 27, 2021
GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease AssociationsCunmei Ji, Zhihao Liu, Yutian Wang, et al.
Frontiers in Genetics|September 13, 2021
Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention NetworksCunmei Ji, Yutian Wang, Jiancheng Ni, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 6, 2024
SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association PredictionCunmei Ji, Ning Yu, Yutian Wang, et al.
Genes|June 24, 2022
MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA-Disease Association PredictionJiancheng Ni, Lei Li, Yutian Wang, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|September 10, 2024
Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing DataShengwen Tian, Cunmei Ji, Jiancheng Ni, et al.
Bioinformatics (Oxford, England)|July 30, 2020
AEMDA: inferring miRNA-disease associations based on deep autoencoderCunmei Ji, Zhen Gao, Xu Ma, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 18, 2021
A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational AutoencoderCunmei Ji, Yutian Wang, Zhen Gao, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Frontiers in Genetics|May 1, 2020
MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease AssociationQingwen Wu, Yutian Wang, Zhen Gao, et al.
International Journal of Molecular Sciences|August 27, 2021
GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease AssociationsCunmei Ji, Zhihao Liu, Yutian Wang, et al.
Frontiers in Genetics|September 13, 2021
Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention NetworksCunmei Ji, Yutian Wang, Jiancheng Ni, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 6, 2024
SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association PredictionCunmei Ji, Ning Yu, Yutian Wang, et al.
Genes|June 24, 2022
MDSCMF: Matrix Decomposition and Similarity-Constrained Matrix Factorization for miRNA-Disease Association PredictionJiancheng Ni, Lei Li, Yutian Wang, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|September 10, 2024
Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing DataShengwen Tian, Cunmei Ji, Jiancheng Ni, et al.
Bioinformatics (Oxford, England)|July 30, 2020
AEMDA: inferring miRNA-disease associations based on deep autoencoderCunmei Ji, Zhen Gao, Xu Ma, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics|March 18, 2021
A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational AutoencoderCunmei Ji, Yutian Wang, Zhen Gao, et al.
Pageof 1