Contrastive Learning Drug Response Models from Natural Language Supervision
Authors: Kun Li, Xiuwen Gong, Jia Wu, Wenbin Hu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the practical evaluation, we validate the method using a dataset comprising over 150,000 samples from the GDSC2 dataset. Consequently, all the methods display increases of 7.88%, 19.49%, 17.83%, 14.29%, 13.04%, and 31.46% after adopting our framework. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University, Wuhan, China 2UTS Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia 3Department of Computing, Macquarie University, Sydney, Australia |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at: https://github.com/Drug D/ CLDR. |
| Open Datasets | Yes | For the practical evaluation, we validate the method using a dataset comprising over 150,000 samples from the GDSC2 dataset. |
| Dataset Splits | Yes | Then, the data was randomly divided into training, validation, and testing sets by a ratio of 8:1:1 and the drug type as the division standard. |
| Hardware Specification | Yes | The experiment utilized an Intel Xeon E5-2690 v3 processor with 12 cores (i.e., 24 threads) and a clock frequency of 2.60GHz. Additionally, the RTX 4090 GPU was utilized. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | No | The paper describes the validation strategy, dataset splitting, and metrics used, but does not provide specific hyperparameter values or detailed system-level training settings for reproduction. |