Tiangang Zhang
Orcid: 0000-0002-7528-3581
According to our database1,
Tiangang Zhang
authored at least 37 papers
between 2018 and 2024.
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Bibliography
2024
Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.
IEEE J. Biomed. Health Informatics, July, 2024
Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.
Bioinform., February, 2024
Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.
J. Chem. Inf. Model., 2024
Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.
J. Chem. Inf. Model., 2024
Gating-Enhanced Hierarchical Structure Learning in Hyperbolic Space and Multi-scale Neighbor Topology Learning in Euclidean Space for Prediction of Microbe-Drug Associations.
J. Chem. Inf. Model., 2024
Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.
Comput. Biol. Medicine, 2024
Appl. Soft Comput., 2024
2023
Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations.
J. Chem. Inf. Model., November, 2023
Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction.
Comput. Biol. Medicine, September, 2023
Multi-scale random walk driven adaptive graph neural network with dual-head neighboring node attention for CT segmentation.
Appl. Soft Comput., January, 2023
Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023
2022
IEEE J. Biomed. Health Informatics, 2022
Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.
IEEE J. Biomed. Health Informatics, 2022
Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022
Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes.
Knowl. Based Syst., 2022
Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.
Comput. Methods Programs Biomed., 2022
Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction.
Briefings Bioinform., 2022
multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.
Briefings Bioinform., 2022
Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction.
Briefings Bioinform., 2022
Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs.
Briefings Bioinform., 2022
Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction.
Briefings Bioinform., 2022
Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction.
Briefings Bioinform., 2022
Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs.
Briefings Bioinform., 2022
GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.
Briefings Bioinform., 2022
ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.
Briefings Bioinform., 2022
Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.
Briefings Bioinform., 2022
2021
Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations.
IEEE J. Biomed. Health Informatics, 2021
Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
Integrating multi-scale neighbouring topologies and cross-modal similarities for drug-protein interaction prediction.
Briefings Bioinform., 2021
Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction.
Briefings Bioinform., 2021
Integrating Channel Context Attention and Regional Association Attention for Kidney and Tumor Segmentation.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
2020
Inferring Disease-Associated microRNAs in Heterogeneous Networks with Node Attributes.
IEEE ACM Trans. Comput. Biol. Bioinform., 2020
Assistant diagnosis with Chinese electronic medical records based on CNN and BiLSTM with phrase-level and word-level attentions.
BMC Bioinform., 2020
2019
Drug repositioning through integration of prior knowledge and projections of drugs and diseases.
Bioinform., 2019
2018
A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network.
Bioinform., 2018