Kai Zheng
Orcid: 0000-0003-1578-1818Affiliations:
- China University of Mining and Technology, School of Computer Science and Technology, Xuzhou, China
According to our database1,
Kai Zheng
authored at least 20 papers
between 2019 and 2023.
Collaborative distances:
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Bibliography
2023
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.
Briefings Bioinform., January, 2023
2022
Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.
Briefings Bioinform., 2022
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
MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.
Briefings Bioinform., 2021
2020
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.
BMC Medical Informatics Decis. Mak., March, 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
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
An Efficient LightGBM Model to Predict Protein Self-interacting Using Chebyshev Moments and Bi-gram.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins.
Proceedings of the Intelligent Computing Theories and Application, 2019
Predicting of Drug-Disease Associations via Sparse Auto-Encoder-Based Rotation Forest.
Proceedings of the Intelligent Computing Methodologies - 15th International Conference, 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