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Machine Learning for Diagnosis of Diseases with Complete Gene Expression Profile

  • INTELLECTUAL CONTROL SYSTEMS, DATA ANALYSIS
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Abstract

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification.

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Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    A. M. Mikhailov, M. F. Karavai & V. A. Sivtsov

  2. Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology, and Immunology, Moscow, Russia

    M. A. Kurnikova

Authors
  1. A. M. Mikhailov
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  2. M. F. Karavai
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  3. V. A. Sivtsov
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  4. M. A. Kurnikova
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Corresponding authors

Correspondence to A. M. Mikhailov, M. F. Karavai, V. A. Sivtsov or M. A. Kurnikova.

Additional information

This paper was recommended for publication by O.N. Granichin, a member of the Editorial Board

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Mikhailov, A.M., Karavai, M.F., Sivtsov, V.A. et al. Machine Learning for Diagnosis of Diseases with Complete Gene Expression Profile. Autom Remote Control 84, 727–733 (2023). https://doi.org/10.1134/S0005117923070093

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  • DOI: https://doi.org/10.1134/S0005117923070093

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