AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction
Authors: Hao Chen, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, Qing He
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two databases show that our method can significantly improve the diagnostic accuracy for Autism spectrum disorder and breast cancer, indicating its universality in leveraging multimodal data for disease prediction. |
| Researcher Affiliation | Collaboration | 1Zhengzhou University, Zhengzhou, China 2Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 3Institute of Artificial Intelligence, Beihang University, Beijing 100191, China 4The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China 5Ningbo Huamei Hospital, University of the Chinese Academy of Sciences, Ningbo, China 6Xiamen Data Intelligence Academy of ICT, CAS, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include a specific repository link or explicit code release statement. |
| Open Datasets | Yes | To verify the effectiveness of our model, we evaluate it on the Autism Brain Imaging Data Exchange (ABIDE) database [Martino et al., 2014]. |
| Dataset Splits | Yes | We employ 10-fold cross-validation to evaluate the performance of the model and implement our model using Tensor Flow. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states, "implement our model using Tensor Flow," but does not specify a version number for TensorFlow or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The hyperparameters of the experiment are shown in Table 1. Hyperparameter description Value Layer number of the MLA-GCN 5 Layer number of the ADU-GCN 2 Chebyshev polynomial 3 Number of node features 2000 Graph convolution kernel 16 Learning rate of MLA-GCN 0.005 Learning rate of ADU-GCN 0.05 Regularization parameter 0.0005 Dropout probability 0.3 Number of training epoch 300 Tradeoff parameter λ 1 Optimizer Adam |