Docking as a way to analyze biomedical data

M. V. Postnova; G. A. Sroslova; A. V. Kovalenko; Y. A. Zimina

doi:10.1117/12.2523104

3 June 2019 Docking as a way to analyze biomedical data

M. V. Postnova, G. A. Sroslova, A. V. Kovalenko, Y. A. Zimina

Proceedings Volume 11067, Saratov Fall Meeting 2018: Computations and Data Analysis: from Nanoscale Tools to Brain Functions; 110670M (2019) https://doi.org/10.1117/12.2523104
Event: International Symposium on Optics and Biophotonics VI: Saratov Fall Meeting 2018, 2018, Saratov, Russian Federation

Abstract

The paper demonstrates the importance of neural networks, which are successfully used in various fields. Artificial neural networks demonstrate a large number of brain properties. They are trained on the basis of experience, generalize previous precedents to new cases and extract significant properties from incoming information which contains excessive data. Technically, training is to find coefficients of connections between neurons. In the process of learning, a neural network is able to detect complex dependencies between input and output data, and also perform generalization. As a result, the analysis showed that, on average, the neural network made 50% of forecasts.

Citation Download Citation

M. V. Postnova, G. A. Sroslova, A. V. Kovalenko, and Y. A. Zimina "Docking as a way to analyze biomedical data", Proc. SPIE 11067, Saratov Fall Meeting 2018: Computations and Data Analysis: from Nanoscale Tools to Brain Functions, 110670M (3 June 2019); https://doi.org/10.1117/12.2523104

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