Zero-shot Learning for Preclinical Drug Screening
Authors: Kun Li, Weiwei Liu, Yong Luo, Xiantao Cai, Jia Wu, Wenbin Hu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of experiments on two large drug response datasets showed that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase. |
| Researcher Affiliation | Academia | 1School of Computer Science, Wuhan University, Wuhan, China 2Department of Computing, Macquarie University, Sydney, Australia {li__kun, luoyong, caixiantao, hwb}@whu.edu.com, liuweiwei863@gmail.com, jia.wu@mq.edu.au |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Drug D/MSDA. |
| Open Datasets | Yes | We evaluate the performance of the MSDA plug-in on two publicly available datasets: GDSCv2 [Yang et al., 2012] and Cell Miner [Reinhold et al., 2012]. |
| Dataset Splits | Yes | The drug domain is then randomly partitioned into source and target domains in an 8:2 ratio. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not specify the versions of software dependencies or libraries used for the experiments. |
| Experiment Setup | No | The paper discusses some aspects of the experimental setup, such as dataset splits and evaluation metrics, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations required for direct reproduction. |