Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
Authors: Kexin Zhang, Feng Huang, Luotao Liu, Zhankun Xiong, Hongyu Zhang, Yuan Quan, Wen Zhang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph s semantics and structure. |
| Researcher Affiliation | Academia | 1 College of Informatics, Huazhong Agricultural University, Wuhan 430070, China 2 Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. |
| Open Source Code | Yes | Source code and dataset of HCMGNN are available online at https://github.com/zkxinxin/HCMGNN. |
| Open Datasets | Yes | We construct a dataset of gene-microbe-disease associations using several public databases. The gene-microbe associations are collected from GIMICA [Tang et al., 2021] and gut MGene [Cheng et al., 2022b]. The microbe-disease associations are derived from Micro Pheno DB [Yao et al., 2020], Gut MDdisorder [Cheng et al., 2020] and Peryton [Skoufos et al., 2021]. And the gene-disease associations are obtained from the Dis Ge NET [Pi nero et al., 2016]. |
| Dataset Splits | Yes | We randomly split the dataset into 90% cross-validation (CV) set and 10% independent test set. Then we perform the 5fold CV on the CV set to train the model and optimize the hyper-parameters, and evaluate model performance on the independent test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | For training our model, we set the balance coefficient of the loss function to 0.7, employ the Adam optimizer with a learning rate set to 0.005 to optimize the model, and adopt early stopping mechanism with a patience of 50 to terminate the training early (the hyperparameter sensitivity analysis are provided in Appendix Section 4). |