Application of K-Nearest Neighbors Algorithm for Vaccine Target Prediction

Abstract

Vaccine development heavily relies on identifying suitable antigen targets to induce robust immune responses. Traditional methods for vaccine target identification often involve laborious experimental procedures and may lack scalability. In recent years, machine learning algorithms, particularly the K-Nearest Neighbors (KNN) algorithm, have gained traction in bioinformatics tasks, including vaccine target prediction (Levine et al., 2016; Doytchinova & Flower, 2007). This paper presents an implementation of the KNN algorithm for vaccine target prediction and provides insights into its effectiveness. Leveraging a comprehensive dataset of pathogen proteins, we employ machine learning techniques for feature learning and prediction. Our results demonstrate the promising performance of the KNN models in accurately identifying vaccine targets, surpassing traditional methods in terms of both accuracy and efficiency (Heinson et al., 2017; Magnan et al., 2010). We highlight the key factors influencing model performance through meticulous analysis and discuss potential avenues for further improvement (Goodswen et al., 2013). Overall, our study underscores the potential of the KNN algorithm in vaccine target prediction, offering valuable insights for future research in the field.