Zheng-wei Li

Xuzhou, 221116, China

Last Updated: 09/26/2026

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Found 18 publications

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

Journal: IEEE journal of biomedical and health informatics

Published: December 25, 2023

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

Journal: IEEE journal of biomedical and health informatics

Published: December 21, 2023

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Journal: Journal of chemical information and modeling

Published: December 16, 2023

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

Journal: BMC bioinformatics

Published: August 10, 2023

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

Journal: Computers in biology and medicine

Published: June 04, 2023

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

Journal: iScience

Published: May 19, 2023

DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.

DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.

Journal: iScience

Published: December 12, 2020

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.

Journal: Molecular therapy : the journal of the American Society of Gene Therapy

Published: September 07, 2020

DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.

DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.

Journal: Molecular therapy. Nucleic acids

Published: July 08, 2019

MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

Journal: Journal of translational medicine

Published: May 14, 2019

DRMDA: deep representations-based miRNA-disease association prediction.

DRMDA: deep representations-based miRNA-disease association prediction.

Journal: Journal of cellular and molecular medicine

Published: March 06, 2017

PRMDA: personalized recommendation-based MiRNA-disease association prediction.

PRMDA: personalized recommendation-based MiRNA-disease association prediction.

Journal: Oncotarget

Published: February 15, 2017

Showing 1-12 of 18

Last Updated: 09/26/2026