Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction
Authors: Luotao Liu, Feng Huang, Xuan Liu, Zhankun Xiong, Menglu Li, Congzhi Song, Wen Zhang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that MCHNN achieves satisfactory performance in DMD association prediction and, more importantly, demonstrate the effectiveness of our devised the multi-view CL on the sparse DMD hypergraph. |
| Researcher Affiliation | Academia | College of Informatics, Huazhong Agricultural University, Wuhan 430070, China {luotaoliu, fhuang233, lx666, xiongzk, mengluli, song cz }@webmail.hzau.edu.cn, zhangwen@mail.hzau.edu.cn |
| Pseudocode | No | The paper describes the model architecture and methods in text and using mathematical equations, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | More detailed settings, source code, data set, and additional results of MCHNN are presented in Supplementary materials1. 1https://github.com/Liuluotao/MCHNN |
| Open Datasets | Yes | The drugmicrobe associations are collected from MDAD [Sun et al., 2018], a Biofilm [Rajput et al., 2018], and Drug Virus [Andersen et al., 2020], and the microbe-disease associations are obtained from HMDAD [Ma et al., 2017], Disbiome [Janssens et al., 2018], gut MDisorder [Cheng et al., 2020], and Peryton [Skoufos et al., 2021]. |
| Dataset Splits | Yes | We randomly split the dataset into a 90% cross-validation (CV) set and a 10% independent test set. On the CV set, the 5-fold CV is implemented. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were found in the paper. |
| Software Dependencies | No | The paper mentions software components like 'Deep Chem package' and 'GIN' but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | The number of GIN layer k for drug node attributes and HGNN layer l for hypergraph encoding are both set to 3. In DMD association prediction, the scoring function is a 3-layer MLP with Dropout. We fix α to 0.8 as they produced the best performance in hyper-parameter optimization. Moreover, we employ Adam with a learning rate of 0.005 to optimize the model and adopt early stopping to control the training epochs based on validation loss. |