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..
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
Authors: Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our method, we compiled two new benchmark datasets from Drug Bank and DGIdb. The comprehensive experiments on the two datasets showed that our method outperformed state-of-the-art baselines in the transductive scenarios and achieved superior performance in the inductive ones. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Sun Yat-sen University 2School of Electronic and Information Technology, Sun Yat-sen University 3Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University 4Galixir Technologies Ltd, Beijing |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual explanations, but it does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | Yes | We implemented Co SMIG using pytorch geometric [Fey and Lenssen, 2019], which is available at https://github.com/biomed-AI/Co SMIG. |
| Open Datasets | Yes | To evaluate the effectiveness of Co SMIG, we compiled the multi-relational datasets from DGIdb[Cotto et al., 2018] and Drug Bank [Wishart et al., 2018], respectively (Table 1). |
| Dataset Splits | No | We tuned model hyperparameters based on cross validation results on Drug Bank and used them across all datasets. |
| Hardware Specification | Yes | The training process lasted 80 epochs on a Nvidia Ge Force RTX 3090 GPU. |
| Software Dependencies | No | We implemented Co SMIG using pytorch geometric [Fey and Lenssen, 2019] |
| Experiment Setup | Yes | The hop number h was set to 3. The depth of model was set to 4. For each subgraph, we randomly dropped out its adjacency matrix entries with a probability of 0.1 during the training. The training process lasted 80 epochs on a Nvidia Ge Force RTX 3090 GPU. |