Nedyalkova Miroslava, Vasighi Mahdi, Azmoon Amirreza, Naneva Ludmila, Simeonov Vasil
Department of Chemistry, University of Fribourg, Chemin de Muse 9, CH-1700Fribourg, Switzerland.
Faculty of Chemistry and Pharmacy, Inorganic Chemistry, University of Sofia, 1172Sofia, Bulgaria.
ACS Omega. 2023 Jan 20;8(4):3698-3704. doi: 10.1021/acsomega.2c02842. eCollection 2023 Jan 31.
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 -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.
本文提出了一种新颖的化学计量学方法，用于理解和探索食物蛋白质的致敏性质。利用机器学习方法（有监督和无监督），这项工作旨在预测植物蛋白质的致敏性。该策略基于对描述符进行评分并测试其分类性能。划分基于支持向量机（SVM），并应用了k近邻（KNN）分类器。在变量选择步骤以及最终分类器中，采用五重交叉验证方法来验证KNN分类器。为了克服食物过敏问题，需要一种稳健且高效的蛋白质分类方法。
