Prediction of uncontrolled course of bronchial asthma in children based on polymorphisms of genes of signaling molecules of the immune system and detoxification genes

Aim. To study the possibility of predicting the asthma control at various stages of the development of the disease, possibly on the basis of taking into account the genetic polymorphisms of Toll-like receptors, cytokines and detoxification system genes using the statistical method of learning neural networks.Materials and methods. We ex­amined 167 children with bronchial asthma. The degree of asthma control was determined, the following mutations were detected: TLR2-Arg753Glu, TLR4-Asp299Gly, TLR4-Ghr399Ile, TLR9-T1237C, TLR9-A2848G; IL4-C589T, IL6- C174G, IL10-G1082A, IL10-C592A, IL10-C819T, IL12B-A1188C, TNFa-G308A; GSTM, GSTT, GSTM/GSTT, GSTP1 Ile105Val, GSTP1 Ala114Val, by PCR. The STATISTICA Automated Neural Networks package was used to model neural networks.Results. The model is based on the MLP (15-9-3) multilayer perceptron architecture with a layer of 15 input neurons (by the number of analyzed variables), a hidden intermediate layer of 9 neurons and an output layer of 3 neurons by the number of values of the classified variable (control). The training algorithm was chosen by BFGS as the most adequate to the classification task. The error function is traditionally chosen as the sum of squared deviations. The activation function of output neurons is Softmax. The activation function of the intermediate layer is hyperbolic. The volume of the training sample was 88 sets. The volume of samples for testing and quality control of the model was 36 sets. The resulting model was able to predict 79.01% of the correct values of the target variable (the degree of asthma control).Conclusion. The application of the developed program makes it possible to predict the possibility of uncontrolled or partially controlled asthma at any stage of the disease, including preclinical and pre-nosological for groups with a high risk of asthma. This allows you to individually adjust the measures of secondary and even primary prevention of asthma within the personal­ization of therapeutic approaches.