Gated Graph Attention Network for Cancer Prediction
With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/6/1938 |
| _version_ | 1797541951302533120 |
|---|---|
| author | Linling Qiu Han Li Meihong Wang Xiaoli Wang |
| author_facet | Linling Qiu Han Li Meihong Wang Xiaoli Wang |
| author_sort | Linling Qiu |
| collection | DOAJ |
| description | With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work’s limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods. |
| first_indexed | 2024-03-10T13:22:52Z |
| format | Article |
| id | doaj.art-0c22c85041bd4e2294c0bb073411090a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:22:52Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0c22c85041bd4e2294c0bb073411090a2023-11-21T09:51:48ZengMDPI AGSensors1424-82202021-03-01216193810.3390/s21061938Gated Graph Attention Network for Cancer PredictionLinling Qiu0Han Li1Meihong Wang2Xiaoli Wang3School of Informatics, Xiamen University, Xiamen 361001, ChinaSchool of Informatics, Xiamen University, Xiamen 361001, ChinaSchool of Informatics, Xiamen University, Xiamen 361001, ChinaSchool of Informatics, Xiamen University, Xiamen 361001, ChinaWith its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work’s limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods.https://www.mdpi.com/1424-8220/21/6/1938attention mechanismgating mechanismgraph convolutional networkTCGAcancer prediction |
| spellingShingle | Linling Qiu Han Li Meihong Wang Xiaoli Wang Gated Graph Attention Network for Cancer Prediction Sensors attention mechanism gating mechanism graph convolutional network TCGA cancer prediction |
| title | Gated Graph Attention Network for Cancer Prediction |
| title_full | Gated Graph Attention Network for Cancer Prediction |
| title_fullStr | Gated Graph Attention Network for Cancer Prediction |
| title_full_unstemmed | Gated Graph Attention Network for Cancer Prediction |
| title_short | Gated Graph Attention Network for Cancer Prediction |
| title_sort | gated graph attention network for cancer prediction |
| topic | attention mechanism gating mechanism graph convolutional network TCGA cancer prediction |
| url | https://www.mdpi.com/1424-8220/21/6/1938 |
| work_keys_str_mv | AT linlingqiu gatedgraphattentionnetworkforcancerprediction AT hanli gatedgraphattentionnetworkforcancerprediction AT meihongwang gatedgraphattentionnetworkforcancerprediction AT xiaoliwang gatedgraphattentionnetworkforcancerprediction |