Liping Li
Orcid: 0000-0002-7785-929XAffiliations:
- Xijing University, School of Information Engineering, Xi'an, China
- Chinese Academy of Science (CAS), Xinjiang Technical Institutes of Physics and Chemistry, Urumqi, China
According to our database1,
Liping Li
authored at least 36 papers
between 2009 and 2024.
Collaborative distances:
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Bibliography
2024
MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.
BMC Bioinform., December, 2024
SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.
J. Chem. Inf. Model., January, 2024
RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding.
J. Chem. Inf. Model., 2024
2023
BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path.
J. Chem. Inf. Model., August, 2023
A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.
Briefings Bioinform., May, 2023
Combining K Nearest Neighbor With Nonnegative Matrix Factorization for Predicting Circrna-Disease Associations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
2022
Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.
BMC Bioinform., 2022
A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.
Briefings Bioinform., 2022
2021
Sci. Program., 2021
FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method.
Sci. Program., 2021
LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization.
Neurocomputing, 2021
A computational approach for predicting drug-target interactions from protein sequence and drug substructure fingerprint information.
Int. J. Intell. Syst., 2021
Weighted Nonnegative Matrix Factorization Based on Multi-source Fusion Information for Predicting CircRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2021
Computational Prediction of Protein-Protein Interactions in Plants Using Only Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2021
2020
GANCDA: a novel method for predicting circRNA-disease associations based on deep generative adversarial network.
Int. J. Data Min. Bioinform., 2020
A survey of current trends in computational predictions of protein-protein interactions.
Frontiers Comput. Sci., 2020
Prediction of Drug-Target Interactions by Ensemble Learning Method From Protein Sequence and Drug Fingerprint.
IEEE Access, 2020
GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions.
IEEE Access, 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
WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations.
Proceedings of the Intelligent Computing Methodologies - 16th International Conference, 2020
Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform.
Proceedings of the Intelligent Computing Theories and Application, 2020
Inferring Drug-miRNA Associations by Integrating Drug SMILES and MiRNA Sequence Information.
Proceedings of the Intelligent Computing Theories and Application, 2020
2019
An Efficient Ensemble Learning Approach for Predicting Protein-Protein Interactions by Integrating Protein Primary Sequence and Evolutionary Information.
IEEE ACM Trans. Comput. Biol. Bioinform., 2019
LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.
PLoS Comput. Biol., 2019
A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel.
Proceedings of the Intelligent Computing Theories and Application, 2019
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations.
Proceedings of the Intelligent Computing Theories and Application, 2019
Combining LSTM Network Model and Wavelet Transform for Predicting Self-interacting Proteins.
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
Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine.
Complex., 2018
Efficient Framework for Predicting ncRNA-Protein Interactions Based on Sequence Information by Deep Learning.
Proceedings of the Intelligent Computing Theories and Application, 2018
RP-FIRF: Prediction of Self-interacting Proteins Using Random Projection Classifier Combining with Finite Impulse Response Filter.
Proceedings of the Intelligent Computing Theories and Application, 2018
2013
Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor.
Proceedings of the 2013 IEEE Workshop on Memetic Computing, 2013
Research on Signaling Pathways Reconstruction by Integrating High Content RNAi Screening and Functional Gene Network.
Proceedings of the Intelligent Computing Theories and Technology, 2013
2010
Increasing Reliability of Protein Interactome by Combining Heterogeneous Data Sources with Weighted Network Topological Metrics.
Proceedings of the Advanced Intelligent Computing Theories and Applications, 2010
2009
Integration of Genomic and Proteomic Data to Predict Synthetic Genetic Interactions Using Semi-supervised Learning.
Proceedings of the Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, 2009