Technology of Informative Feature Selection for Immunosignature Analysis.

Journal: Sovremennye tekhnologii v meditsine
Published Date: Oct 28, 2020

Abstract

UNLABELLED: The main difficulty in practical work with data obtained via immunosignature analysis is high dimensionality and the presence of a significant number of uninformative or false-informative features due to the specific character of the technology. To ensure practically relevant quality of data analysis and classification, it is necessary to take due account of this specific character. is to create and test the technology for effective reduction of immunosignature data dimensionality, which provides practically relevant and high quality of classification with due regard for the properties of the data obtained.

Authors

  • A A Koshechkin
    Assistant, Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia.
  • O V Romanovich
    Associate Professor, Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia; Leading Engineer, Institute of Applied Mathematics and Computer Science; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia.
  • D Stamate
    Senior Lecturer; Data Science Department of Computing, Goldsmiths, University of London, New Cross, London, SE14 6NW, UK.
  • S A Johnston
    Center Director and Professor; Biodesign Center for Innovations in Medicine, Arizona State University, Tempe, AZ 85281, USA.
  • A V Zamyatin
    Head of the Department of Theoretical Foundations of Informatics; National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia; Director of the Institute of Applied Mathematics and Computer Science National Research Tomsk State University, 36 Lenin Avenue, Tomsk, 634050, Russia.

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