Machine Learning for Diagnosis of Diseases with Complete Gene Expression Profile

• Published in: Automation and remote control Vol. 84; no. 7; pp. 727 - 733
• Main Authors: Mikhailov, A. M., Karavai, M. F., Sivtsov, V. A., Kurnikova, M. A.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.07.2023; Springer Nature B.V
• Subjects: CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Computer-Aided Engineering (CAD; Control; Data Analysis; Diagnosis; Documents; Gene expression; Genes; Intellectual Control Systems; Machine learning; Mathematics; Mathematics and Statistics; Mechanical Engineering; Mechatronics; Neural networks; Pattern recognition; Robotics; Search engines; Systems Theory; diagnosis of diseases; machine learning; gene expression profiles; pattern recognition; inverse patterns
• ISSN: 0005-1179; 1608-3032
• DOI: 10.1134/S0005117923070093

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.