Результаты поиска по 'Potts model':
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  1. Grebenkin I.V., Alekseenko A.E., Gaivoronskiy N.A., Ignatov M.G., Kazennov A.M., Kozakov D.V., Kulagin A.P., Kholodov Y.A.
    Ensemble building and statistical mechanics methods for MHC-peptide binding prediction
    Computer Research and Modeling, 2020, v. 12, no. 6, pp. 1383-1395

    The proteins of the Major Histocompatibility Complex (MHC) play a key role in the functioning of the adaptive immune system, and the identification of peptides that bind to them is an important step in the development of vaccines and understanding the mechanisms of autoimmune diseases. Today, there are a number of methods for predicting the binding of a particular MHC allele to a peptide. One of the best such methods is NetMHCpan-4.0, which is based on an ensemble of artificial neural networks. This paper presents a methodology for qualitatively improving the underlying neural network underlying NetMHCpan-4.0. The proposed method uses the ensemble construction technique and adds as input an estimate of the Potts model taken from static mechanics, which is a generalization of the Ising model. In the general case, the model reflects the interaction of spins in the crystal lattice. Within the framework of the proposed method, the model is used to better represent the physical nature of the interaction of proteins included in the complex. To assess the interaction of the MHC + peptide complex, we use a two-dimensional Potts model with 20 states (corresponding to basic amino acids). Solving the inverse problem using data on experimentally confirmed interacting pairs, we obtain the values of the parameters of the Potts model, which we then use to evaluate a new pair of MHC + peptide, and supplement this value with the input data of the neural network. This approach, combined with the ensemble construction technique, allows for improved prediction accuracy, in terms of the positive predictive value (PPV) metric, compared to the baseline model.

    Keywords: major histocompatibility complex, binding affinity, neural network, machine learning, Potts model.

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