Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection
Authors: Song Li, Yang Tan, Song Ke, liang hong, Bingxin Zhou
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that VENUSVACCINE outperforms existing methods across a wide range of evaluation metrics. |
| Researcher Affiliation | Collaboration | 1 School of Physics and Astronomy, Shanghai Jiao Tong University. 2 School of Information Science and Engineering, East China University of Science and Technology. 3 Shanghai Matwings Technology Co., Ltd. |
| Pseudocode | No | The paper describes methods narratively and with equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation is at https://github.com/songleee/Venus Vaccine. |
| Open Datasets | No | All source data and the implementation to reproduce the results will be publicly available upon acceptance. |
| Dataset Splits | Yes | For each dataset, we randomly split the data in a 7 : 1 : 2 ratio and select the model with the highest accuracy on the 10% validation set for evaluation. |
| Hardware Specification | Yes | All implementations were done using Py Torch (version 1.7.0), and experiments were run on an NVIDIAr RTX 3090 GPU with 24GB VRAM, mounted on a Linux server. |
| Software Dependencies | Yes | All implementations were done using Py Torch (version 1.7.0) |
| Experiment Setup | Yes | The model is optimized using ADAMW (Loshchilov et al., 2017) with a learning rate of 0.0005 and a weight decay of 0.01. In the attention pooling layer, dropout was set to 0.1. The maximum training epoch was set to 50, with early stopping based on validation accuracy and patience of 5. |