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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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). |