论文标题
部分可观测时空混沌系统的无模型预测
Constrained Submodular Optimization for Vaccine Design
论文作者
论文摘要
机器学习的进展使得可以预测免疫系统对预防性和治疗性疫苗的反应。但是，设计疫苗的工程任务仍然是一个挑战。特别是，人免疫系统的遗传变异性使设计肽疫苗很难在接种疫苗的种群中提供广泛的免疫力。我们介绍了一个框架，用于评估和设计使用概率机器学习模型的肽疫苗，并展示其为SARS-COV-2疫苗设计设计的能力，该疫苗的表现优于先前的设计。我们提供了框架的近似性，可扩展性和复杂性的理论分析。
Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the human immune system makes it difficult to design peptide vaccines that provide widespread immunity in vaccinated populations. We introduce a framework for evaluating and designing peptide vaccines that uses probabilistic machine learning models, and demonstrate its ability to produce designs for a SARS-CoV-2 vaccine that outperform previous designs. We provide a theoretical analysis of the approximability, scalability, and complexity of our framework.
