Hui Liu
Orcid: 0000-0001-7158-913XAffiliations:
- Changzhou University, Lab of Information Management, Jiangsu, China
- Fudan University, School of Computer Science, Shanghai, China (PhD 2010)
According to our database1,
Hui Liu
authored at least 31 papers
between 2010 and 2024.
Collaborative distances:
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Bibliography
2024
Contrastive learning-based histopathological features infer molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images.
Comput. Biol. Medicine, March, 2024
2022
Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity.
J. Chem. Inf. Model., 2022
MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.
BMC Bioinform., 2022
Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations.
Bioinform., 2022
SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.
Database J. Biol. Databases Curation, 2022
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.
Briefings Bioinform., 2022
Briefings Bioinform., 2022
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes.
Briefings Bioinform., 2022
2021
A multi-task graph convolutional network modeling of drug-drug interactions and synergistic efficacy.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021
2020
DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy.
Nucleic Acids Res., 2020
HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features.
J. Chem. Inf. Model., 2020
Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity.
J. Chem. Inf. Model., 2020
Predicting circRNA-disease associations using meta path-based representation learning on heterogenous network.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020
2019
Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.
BMC Bioinform., 2019
MADOKA: an ultra-fast approach for large-scale protein structure similarity searching.
BMC Bioinform., 2019
D2VCB: A Hybrid Deep Neural Network for the Prediction of in-vivo Protein-DNA Binding from Combined DNA Sequence.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019
A deep neural network approach using distributed representations of RNA sequence and structure for identifying binding site of RNA-binding proteins.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019
2018
Effectively Identifying Compound-Protein Interactions by Learning from Positive and Unlabeled Examples.
IEEE ACM Trans. Comput. Biol. Bioinform., 2018
PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.
BMC Bioinform., 2018
XPredRBR: Accurate and Fast Prediction of RNA-Binding Residues in Proteins Using eXtreme Gradient Boosting.
Proceedings of the Bioinformatics Research and Applications - 14th International Symposium, 2018
2017
Knowl. Based Syst., 2017
Selecting high-quality negative samples for effectively predicting protein-RNA interactions.
BMC Syst. Biol., 2017
2016
Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks.
BMC Syst. Biol., 2016
Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks.
BMC Bioinform., 2016
2015
Improving compound-protein interaction prediction by building up highly credible negative samples.
Bioinform., 2015
2014
The centrality of cancer proteins in human protein-protein interaction network: a revisit.
Int. J. Comput. Biol. Drug Des., 2014
Briefings Bioinform., 2014
2013
IEEE J. Biomed. Health Informatics, 2013
Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks.
BMC Bioinform., 2013
2012
Effectively predicting protein functions by collective classification - An extended abstract.
Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2012
2010
Int. J. Data Min. Bioinform., 2010