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 [1].
Multilevel Attention Network with Semi-supervised Domain Adaptation for Drug-Target Prediction
Authors: Zhousan Xie, Shikui Tu, Lei Xu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four datasets show that Mlan DTI achieves state-of-the-art performances over other methods under intra-domain settings and outperforms all other approaches under cross-domain settings. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2Guangdong Institute of Intelligence Science and Technology, Zhuhai, Guangdong 519031, China EMAIL |
| Pseudocode | No | The paper describes procedures and architecture using text and diagrams, but it does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/CMACH508/Mlan DTI. |
| Open Datasets | Yes | Datasets We evaluated our model on the human dataset, Caenorhabditis elegans dataset (Tsubaki, Tomii, and Sese 2019), bindingdb dataset (Liu et al. 2007), and Biosnap dataset (Huang et al. 2021). |
| Dataset Splits | Yes | For the intra-domain evaluation, we randomly split the dataset into training, validation, and test sets with a ratio of 8:1:1 in smaller human and C.elegans datasets, and 7:1:2 in larger Binding DB and Biosnap datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | While 'Py Torch' and 'Adam optimizer' are mentioned, specific version numbers for these software dependencies are not provided in the paper. |
| Experiment Setup | Yes | Our proposed method in implemented in Py Torch, utilizing the Adam optimizer with an initial learning rate of 0.001. Detailed hyperparameter settings are provided in the appendix. |