Peptide Vaccine Design by Evolutionary Multi-Objective Optimization
Authors: Dan-Xuan Liu, Yi-Heng Xu, Chao Qian
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a peptide vaccine design for COVID-19, caused by the SARS-Co V-2 virus, demonstrate the superiority of PVD-EMO. |
| Researcher Affiliation | Academia | Dan-Xuan Liu , Yi-Heng Xu and Chao Qian National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China School of Artificial Intelligence, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 PVD-EMO Framework; Algorithm 2 PVD-GSEMO-WR Algorithm; Algorithm 3 Warm-Start Strategy; Algorithm 4 Repair Strategy |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We use the same dataset of producing a peptide vaccine for COVID-19 as in [Dai and Gifford, 2023], which consists of a set of candidate peptides (|V | = 1043), a set of genotypes (|M| = 1018459) for Major Histocompatibility Complex class I (MHC-I), their frequencies w(m) derived from diverse populations to be representative of the global population, and the binding probability pv,m for each peptide-MHC pair, generated by the SOTA neural network-based model Net MHCpan [Reynisson et al., 2020]. |
| Dataset Splits | No | The paper describes the dataset used and how some probabilities were generated, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Net MHCpan [Reynisson et al., 2020]' as a model used to generate binding probabilities, and refers to algorithms like GSEMO and NSGA-II. However, it does not provide specific version numbers for any software, libraries, or solvers used in its implementation or experiments. |
| Experiment Setup | Yes | The population size of PVD-NSGA-II-WR is set to 2 times the size of the Pareto front, i.e., 2(k + 1). PVD-NSGA-II-WR applies one-point crossover and bit-wise mutation with probabilities of 0.9 and 1, respectively. As PVD-EMO is an anytime algorithm, whose performance will be gradually improved by increasing the number of iterations, we set the number of objective evaluations to 20kn, to make a trade-off between the performance and runtime, compared to kn evaluations used by Optivax-P. |