| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Guangya Yu, Qi Ye, Tong Rua. Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label. Bioengineering (Basel, Switzerland). vol 11. issue 3. 2024-03-27. PMID:38534499. |
inspired by the success of graph attention networks (gats), we introduce the hyper-view gat to incorporate label messages and neighborhood information into representation learning. |
2024-03-27 |
2024-03-29 |
Not clear |
| Dwarikanath Mahapatra, Behzad Bozorgtabar, Zongyuan Ge, Mauricio Reye. GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification. Medical image analysis. vol 93. 2024-01-10. PMID:38199069. |
we identify the most informative samples by aggregating gat representations. |
2024-01-10 |
2024-01-13 |
Not clear |
| Cary Xiao, Erik A Imel, Nam Pham, Xiao Lu. Patient-GAT: Sarcopenia Prediction using Multi-modal Data Fusion and Weighted Graph Attention Networks. Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing. vol 2023. 2023-12-21. PMID:38125287. |
however, few papers have investigated the effectiveness of using gat on graph representations of patient similarity networks. |
2023-12-21 |
2023-12-23 |
Not clear |
| Cary Xiao, Erik A Imel, Nam Pham, Xiao Lu. Patient-GAT: Sarcopenia Prediction using Multi-modal Data Fusion and Weighted Graph Attention Networks. Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing. vol 2023. 2023-12-21. PMID:38125287. |
this data representation is then used to construct a patient network by measuring patient similarity, finally applying gat to the patient network for disease prediction. |
2023-12-21 |
2023-12-23 |
Not clear |
| Sheng-Wen Tian, Jian-Cheng Ni, Yu-Tian Wang, Chun-Hou Zheng, Cun-Mei J. scGCC: Graph Contrastive Clustering with Neighborhood Augmentations for scRNA-seq Data Analysis. IEEE journal of biomedical and health informatics. vol PP. 2023-09-26. PMID:37751336. |
we introduce graph attention networks (gat) for cell representation learning, which enables better feature extraction and improved clustering accuracy. |
2023-09-26 |
2023-10-07 |
Not clear |
| Yue Wang, Han Sun, Haodong Wang, Dandan Li, Weizhong Zhao, Xingpeng Jiang, Xianjun She. An Effective Model for Predicting Phage-host Interactions via Graph Embedding Representation Learning with Multi-head Attention Mechanism. IEEE journal of biomedical and health informatics. vol PP. 2023-04-08. PMID:37030796. |
more specifically, a module of gat with talking-heads is employed to learn representations of phages and bacteria, on which neural induction matrix completion is conducted to reconstruct the phage-host association matrix. |
2023-04-08 |
2023-08-14 |
human |
| Renping Yu, Cong Pan, Xuan Fei, Mingming Chen, Dinggang She. Multi-Graph Attention Networks with Bilinear Convolution for Diagnosis of Schizophrenia. IEEE journal of biomedical and health informatics. vol PP. 2023-04-05. PMID:37018590. |
graph attention network (gat), which could capture the local stationary on the network topology and aggregate the features of neighboring nodes, has advantages in learning the feature representation of brain regions. |
2023-04-05 |
2023-08-14 |
Not clear |
| Lei Xu, Shourun Pan, Leiming Xia, Zhen L. Molecular Property Prediction by Combining LSTM and GAT. Biomolecules. vol 13. issue 3. 2023-03-29. PMID:36979438. |
the embedding atoms are obtained through salstm, firstly using smiles strings, and they are combined with graph node features and fed into the gat to extract the global molecular representation. |
2023-03-29 |
2023-08-14 |
Not clear |
| Muhammad Umair, Iftikhar Alam, Atif Khan, Inayat Khan, Niamat Ullah, Mohammad Yusuf Moman. N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization. Computational intelligence and neuroscience. vol 2022. 2022-12-02. PMID:36458230. |
inspired by the popularity of transformer-based bidirectional encoder representations (bert) pretrained linguistic model and graph attention network (gat) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model n-gpets by combining heterogeneous graph attention network with bert model along with statistical approach using tf-idf values for extractive summarization task. |
2022-12-02 |
2023-08-14 |
Not clear |
| Minghua Zhao, Min Yuan, Yaning Yang, Steven X X. CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2022-11-29. PMID:36445995. |
in this manuscript, we integrated a graph attention network (gat) for compounds and a long short-term memory neural network (lstm) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, cpgl (cpi with gat and lstm) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. |
2022-11-29 |
2023-08-14 |
human |
| Qiguo Dai, Ziqiang Liu, Zhaowei Wang, Xiaodong Duan, Maozu Gu. GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs. Briefings in bioinformatics. 2022-09-07. PMID:36070619. |
graphcda: a hybrid graph representation learning framework based on gcn and gat for predicting disease-associated circrnas. |
2022-09-07 |
2023-08-14 |
Not clear |
| Guanghui Li, Yawei Lin, Jiawei Luo, Qiu Xiao, Cheng Lian. GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning. Computational biology and chemistry. vol 99. 2022-07-10. PMID:35810557. |
in detail, gat is designed to learn the hidden representations of circrnas and diseases through using low-order neighbor information from circrna similarity network and disease similarity network respectively, while rwr is employed to learn the latent features of circrnas and diseases via using high-order neighbor information from the same two networks respectively. |
2022-07-10 |
2023-08-14 |
human |
| Guanghui Li, Yawei Lin, Jiawei Luo, Qiu Xiao, Cheng Lian. GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning. Computational biology and chemistry. vol 99. 2022-07-10. PMID:35810557. |
unlike previous models, ggaecda deeply mines low-dimensional representations from node similarity network through using gat and rwr. |
2022-07-10 |
2023-08-14 |
human |
| Jongmo Kim, Mye Soh. Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments. Sensors (Basel, Switzerland). vol 22. issue 4. 2022-02-26. PMID:35214446. |
the last one is a method of activity-pattern graph representation based on a gaussian mixture model and kl divergence for training the gat model to detect depression early. |
2022-02-26 |
2023-08-13 |
Not clear |
| Chunde Yang, Panyu Wang, Jia Tan, Qingshui Liu, Xinwei L. Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Computers in biology and medicine. vol 139. 2021-12-03. PMID:34700253. |
in this paper, we proposed a new method named pearson's correlation-based spatial constraints representation (pscr) to estimate the fbn structures that were transformed to brain graphs and then fed into a graph attention network (gat) to diagnose asd. |
2021-12-03 |
2023-08-13 |
Not clear |
| Yang Li, Jingyu Liu, Yiqiao Jiang, Yu Liu, Baiying Le. Virtual Adversarial Training based Deep Feature Aggregation Network from Dynamic Effective Connectivity for MCI Identification. IEEE transactions on medical imaging. vol PP. 2021-09-08. PMID:34491896. |
finally, we propose the high-order connectivity weight-guided graph attention networks (cwgat) to aggregate features of dec. by injecting the weight information of high-order connectivity into the attention mechanism, the cwgat provides more effective high-level feature representations than the conventional gat. |
2021-09-08 |
2023-08-13 |
Not clear |
| Lu Ye, Yi Zhang, Xinying Yang, Fei Shen, Bo X. An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods. Frontiers in cell and developmental biology. vol 9. 2021-09-07. PMID:34485310. |
we first employed graph attention network (gat) to obtain a compact gene feature representation, then a deep neural network (dnn) is utilized to predict oc-related genes. |
2021-09-07 |
2023-08-13 |
Not clear |
| Chen Bian, Xiu-Juan Lei, Fang-Xiang W. GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network. Cancers. vol 13. issue 11. 2021-06-15. PMID:34070678. |
gat learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. |
2021-06-15 |
2023-08-13 |
Not clear |