Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly developing artificial intelligence technology to medical data and providing support for the medical industry is a hot research topic. Cytomegalovirus (CMV) is a kind of virus that exists widely in nature with strict species specificity, and the infection rate among Chinese adults is more than 95%. Therefore, the detection of CMV is of great importance since the vast majority of infected patients are in a state of invisible infection after the infection, except for a few patients with clinical symptoms. In this study, we present a new method to detect CMV infection status by analyzing high-throughput sequencing results of T cell receptor beta chains (TCRβ). Based on the high-throughput sequencing data of 640 subjects from cohort 1, Fisher's exact test was performed to evaluate the relationship between TCRβ sequences and CMV status. Furthermore, the number of subjects with these correlated sequences to different degrees in cohort 1 and cohort 2 were measured to build binary classifier models to identify whether the subject was CMV positive or negative. We select four binary classification algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), and linear discriminant analysis (LDA) for side-by-side comparison. According to the performance of different algorithms corresponding to different thresholds, four optimal binary classification algorithm models are obtained. The logistic regression algorithm performs best when Fisher's exact test threshold is 10