SHINE: SubHypergraph Inductive Neural nEtwork
Authors: Yuan Luo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated SHINE against a wide array of state-of-the-art (hyper)graph neural networks, XGBoost, NMF and polygenic risk score models, using large scale NGS and curated datasets. |
| Researcher Affiliation | Academia | Yuan Luo Feinberg School of Medicine Northwestern University Chicago, IL 60611 yuan.luo@northwestern.edu |
| Pseudocode | No | The paper describes the model's steps mathematically but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | We share our source code at https://github.com/luoyuanlab/SHINE. |
| Open Datasets | Yes | The Dis Ge Net and the TCGA-MC3 datasets are publicly available1, and this study is approved by Northwestern University Institutional Review Board. 1Dis Ge Net: https://www.disgenet.org/; TCGA-MC3: https://gdc.cancer.gov/about-data/publications/mc32017 |
| Dataset Splits | Yes | We used 6:2:2 train:validation:test partition, and the split distribution is shown in Appendix. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or cloud instance specifications) used for running experiments were mentioned. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components, such as libraries or frameworks (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | No | While the paper states that validation datasets were used to tune parameters and hyperparameters, the main text and provided appendices do not explicitly list specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |