KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Authors: Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, Xiangxiang Zeng
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented our method and conducted experiments based on several widely-used datasets. Empirical results show that KGNN outperforms the classic and state-of-the-art models. |
| Researcher Affiliation | Academia | 1College of Information Science and Engineering, Hunan University 2College of Computer Science, Chongqing University |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-codes of applying KGNN for topological neighborhood representation between given drug pairs. |
| Open Source Code | Yes | We evaluate our proposed KGNN1 by using two datasets. 1https://github.com/xzenglab/KGNN |
| Open Datasets | Yes | We evaluate our proposed KGNN1 by using two datasets. (1) Drug Bank: we parse the verified DDIs of the provided profile from Drug Bank (V5.1.4)... (2) KEGG-drug: we parse the sources from KEGG and map it to Drug Bank identifiers (IDs)... |
| Dataset Splits | Yes | For both datasets, we randomly divide all approved DDIs as positive samples into training, validation and testing sets in a 8/1/1 ratio |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing instances. |
| Software Dependencies | No | The paper mentions tools like 'Bio2RDF' and algorithms like 'Adam algorithm' but does not specify version numbers for general software dependencies such as programming languages, libraries (e.g., PyTorch, TensorFlow), or operating systems. |
| Experiment Setup | Yes | Parameter Setting Batch size 4096 Dimension 32 Learning rate 1e-2 Number of depth 2 L2 weight 1e-7 Neighborhood size 16 |