Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

Introduction: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health.

Materials And Methods: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype HN].

Introduction: The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

CYTOCON: The manually curated database of human in vivo cell and molecule concentrations.

Cell and cYTOkine CONcentrations DataBase (CYTOCON DB) is a project undertaken by the InSysBio team and aimed at the development of a database that allows collecting, processing, and visualizing publicly available in vivo human data on baseline concentrations of cells, cytokines, chemokines, and other molecules. Besides manual curation, an important feature of CYTOCON is that most values found in papers are converted from a huge variety of original units (i.e.