Lei Wang
Orcid: 0000-0003-0184-307XAffiliations:
- Zaozhuang University, College of Information Science and Engineering, China
- Chinese Academy of Sciences, Xinjiang Technical Institutes of Physics and Chemistry, Urumqi, China
- China University of Mining and Technology, School of Computer Science and Technology, Jiangsu, China (PhD 2018)
According to our database1,
Lei Wang
authored at least 50 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.
IEEE J. Biomed. Health Informatics, March, 2024
GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.
IEEE J. Biomed. Health Informatics, March, 2024
Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.
Briefings Bioinform., January, 2024
Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning.
IEEE Trans. Emerg. Top. Comput., 2024
LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2024
2023
GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm.
BMC Bioinform., December, 2023
BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path.
J. Chem. Inf. Model., August, 2023
Biomedical Knowledge Graph Embedding With Capsule Network for Multi-Label Drug-Drug Interaction Prediction.
IEEE Trans. Knowl. Data Eng., June, 2023
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.
Briefings Bioinform., January, 2023
PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.
IEEE J. Biomed. Health Informatics, 2023
IEEE Trans. Cybern., 2023
Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
ADARES: A Single-cell Classification Model Based on Adversarial Data Augmentation and Residual Networks.
Proceedings of the 6th International Conference on Signal Processing and Machine Learning, 2023
2022
IEEE J. Biomed. Health Informatics, 2022
Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.
Briefings Bioinform., 2022
HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.
Briefings Bioinform., 2022
iGRLCDA: identifying circRNA-disease association based on graph representation learning.
Briefings Bioinform., 2022
A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction.
Briefings Bioinform., 2022
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2.
Briefings Bioinform., 2022
MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.
Briefings Bioinform., 2022
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.
Briefings Bioinform., 2022
Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022
2021
IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model.
IEEE Trans. Cybern., 2021
MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization.
Neurocomputing, 2021
SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.
Briefings Bioinform., 2021
Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.
Briefings Bioinform., 2021
NMFCDA: Combining randomization-based neural network with non-negative matrix factorization for predicting CircRNA-disease association.
Appl. Soft Comput., 2021
SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning.
Appl. Soft Comput., 2021
CNNEMS: Using Convolutional Neural Networks to Predict Drug-Target Interactions by Combining Protein Evolution and Molecular Structures Information.
Proceedings of the Intelligent Computing Theories and Application, 2021
Predicting miRNA-Disease Associations via a New MeSH Headings Representation of Diseases and eXtreme Gradient Boosting.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.
PLoS Comput. Biol., 2020
GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.
PLoS Comput. Biol., 2020
GANCDA: a novel method for predicting circRNA-disease associations based on deep generative adversarial network.
Int. J. Data Min. Bioinform., 2020
An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network.
Bioinform., 2020
Proceedings of the Intelligent Computing Theories and Application, 2020
Predicting Human Disease-Associated piRNAs Based on Multi-source Information and Random Forest.
Proceedings of the Intelligent Computing Theories and Application, 2020
DTIFS: A Novel Computational Approach for Predicting Drug-Target Interactions from Drug Structure and Protein Sequence.
Proceedings of the Intelligent Computing Theories and Application, 2020
GCNSP: A Novel Prediction Method of Self-Interacting Proteins Based on Graph Convolutional Networks.
Proceedings of the Intelligent Computing Theories and Application, 2020
2019
LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.
PLoS Comput. Biol., 2019
CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM.
IEEE Access, 2019
MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System.
Proceedings of the Intelligent Computing Methodologies - 15th International Conference, 2019
Proceedings of the Intelligent Computing Theories and Application, 2019
Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019
2018
An improved efficient rotation forest algorithm to predict the interactions among proteins.
Soft Comput., 2018
A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.
J. Comput. Biol., 2018
Bioinform., 2018
Bioinform., 2018
2017
Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network.
Proceedings of the Bioinformatics Research and Applications - 13th International Symposium, 2017