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Predictive Factors for Neutralizing Antibody Levels Nine Months after Full Vaccination with BNT162b2: Results of a Machine Learning Analysis

Biomedicines. 2022-01; 
Dimitris Papadopoulos, Ioannis Ntanasis-Stathopoulos, Maria Gavriatopoulou, Zoi Evangelakou, Panagiotis Malandrakis, Maria S Manola, Despoina D Gianniou, Efstathios Kastritis, Ioannis P Trougakos, Meletios A Dimopoulos, Vangelis Karalis, Evangelos Terpos
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摘要

Vaccination against SARS-CoV-2 with BNT162b2 mRNA vaccine plays a critical role in COVID-19 prevention. Although BNT162b2 is highly effective against COVID-19, a time-dependent decrease in neutralizing antibodies (NAbs) is observed. The aim of this study was to identify the individual features that may predict NAbs levels after vaccination. Machine learning techniques were applied to data from 302 subjects. Principal component analysis (PCA), factor analysis of mixed data (FAMD), k-means clustering, and random forest were used. PCA and FAMD showed that younger subjects had higher levels of neutralizing antibodies than older subjects. The effect of age is strongest near the vaccination date and appears to decrea... More

关键词

COVID-19, SARS-CoV-2, factor analysis of mixed data, k-means clustering, machine learning, neutralizing antibodies, principal component analysis, random forest