基于机器学习的蛋白质特征预测b细胞表位区域

Fatema Nafa, Ryan Kanoff
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引用次数: 0

摘要

考虑到Covid-19大流行的现状,疫苗的研究和生产比以往任何时候都更加重要。抗体识别表位,这是抗原的免疫原性区域,以一种非常特定的方式,触发免疫反应。预测这些位置极其困难,但它们对复杂的体液免疫原性途径具有重大意义。本文提出了一种机器学习表位预测模型。该研究创建了几个模型来测试仅基于蛋白质特征的b细胞表位预测的准确性。目标是对XGBoost、CatBoost和LightGbM这三种机器学习模型的准确性进行定量比较。我们的结果发现XGBoost和LightGbM模型之间的准确率相似,其中CatBoost模型的准确率最高,为82%。虽然这种准确性还不够高,不足以被认为是可靠的,但它确实值得对这个问题进行进一步的研究。
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Machine Learning based to Predict B-Cell Epitope Region Utilizing Protein Features
Considering the current state of Covid-19 pandemic, vaccine research and production is more important than ever. Antibodies recognize epitopes, which are immunogenic regions of antigen, in a very specific manner, to trigger an immune response. It is extremely difficult to predict such locations, yet they have substantial implications for complex humoral immunogenicity pathways. This paper presents a machine learning epitope prediction model. The research creates several models to test the accuracy of B-cell epitope prediction based solely on protein features. The goal is to establish a quantitative comparison of the accuracy of three machine learning models, XGBoost, CatBoost, and LightGbM. Our results found similar accuracy between the XGBoost and LightGbM models with the CatBoost model having the highest accuracy of 82%. Though this accuracy is not high enough to be considered reliable it does warrant further research on the subject.
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