Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach

This Article proposes a novel chemometric approach to
understanding
and exploring the allergenic nature of food proteins. Using machine
learning methods (supervised and unsupervised), this work aims to
predict the allergenicity of plant proteins. The strategy is based
on scoring descriptors and testing their classification performance.
Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold
cross-validation approach was used to validate the KNN classifier
in the variable selection step as well as the final classifier. To
overcome the problem of food allergies, a robust and efficient method
for protein classification is needed.

本文提出一种新颖的化学计量学方法，用于解析与探究食品蛋白质的致敏特性。本研究采用监督与无监督机器学习方法，旨在预测植物蛋白质的致敏性。该研究策略通过对描述符进行评分并检验其分类性能得以实现。本研究通过支持向量机（Support Vector Machines, SVM）完成数据集划分，并应用了k近邻（k-nearest neighbor, KNN）分类器。研究采用五折交叉验证方法，在变量选择阶段与最终分类器构建环节均对KNN分类器进行了验证。为应对食品过敏问题，亟需一种稳健且高效的蛋白质分类方法。