MoReL: Multi-omics Relational Learning
Authors: Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Xiaoning Qian
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several real-world datasets demonstrate the enhanced performance of Mo Re L in inferring meaningful interactions compared to existing baselines. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, Texas A&M University |
| Pseudocode | Yes | The pseudo-code in Algorithm 1 (Appendix A.1) provides the details of the FGW distance calculation procedure. |
| Open Source Code | No | The paper does not contain an explicit statement about the availability of source code or a link to a code repository. |
| Open Datasets | Yes | We use the same datasets as Bay Re L (Hajiramezanali et al., 2020), i.e. microbiomemetabolite interactions in cystic fibrosis (CF) and gene-drug interactions in precision medicine. Dataset description and graph construction procedure are detailed in Appendix A.2. (Morton et al., 2019; Quinn et al., 2015; Lee et al., 2018; Wagner et al., 2016) |
| Dataset Splits | Yes | The remaining 20% of the reported molecules are considered as a validation set and are only used for the early stopping purpose. |
| Hardware Specification | Yes | All the experiments are performed on a workstation with a single NVIDIA P100 GPU. |
| Software Dependencies | No | The paper states, "We have implemented Mo Re L and all the competing methods in Tensorflow (Abadi et al., 2015)," but it does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | The temperature for relaxed Bernoulli distribution is set to 0.3. The normalizing parameter γ in equation 6 is 0.9 while α and β in DFGW are set to 1 and 0.5, respectively. We used the exponential decaying learning rate with the decay rate of 0.01 and initial learning rate of 0.01. |