Machine Learning for Diagnosis of Diseases with Complete Gene Expression Profile

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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.

作者简介

A. Mikhaylov

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: alxmikh@gmail.com
Moscow, Russia

M. Karavay

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: mkaravay@yandex.ru
Moscow, Russia

V. Sivtsov

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: thedege@yandex.ru
Moscow, Russia

M. Kurnikova

Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology, and Immunology

编辑信件的主要联系方式.
Email: mish2109@yandex.ru
Moscow, Russia

参考

  1. Khan J., Wei J., Ringner M. et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks // Nat Med. (2001). June 7(6): 673-9. https://doi.org/10.1038/89044
  2. Kumar A., Halder A. Greedy fussy vaguely quantified rough approach for cancer relevant gene selection from gene expression data // Soft Comput. 2022. V. 26. P. 13567-13581. https://doi.org/10.1007/s00500-022-07312-4
  3. Houssein E., Hassan H., Mustafa al-sayed et. al. Gene Selection for Microarray Cancer Classification based on Manta Rays Foraging Optimization and Support Vector Machines // Arabian Journal for Science and Engineering. 2022. V. 47. P. 2555-2572. https://doi.org/10/1007/s13369-021-06101-8
  4. Zheng Y., Sun Y., Kuai Y. et al. Gene expression profiling for the diagnosis of multiple primary malignant tumors // Cancer Cell Int. 2021. V. 21, Article no. 47. https://doi.org/10.1186/s12935-021-01748-8
  5. Ye Q., Wang Q., Qi P. et. al. Development and validation of a 90-gene real-time PCR assay for tumor origin identification // Symposium MXW, 2018.
  6. Joshi P., Dhar R. EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer // Sci. Rep 12, (2022). Article no. 14628. https://doi.org/10.1038/s41598-022-18874-6
  7. Steiling K., Christenson S. Tools for genetics and genomics: Gene expression profiling // UpToDate.(2021). Retrieved from https://www.uptodate.com/contents tools-for-genetics-and-genomics-gene-expression-profiling
  8. СПбГУ Научный парк. Система высокопроизводительного полногеномного секвенирования, 2023. https://researchpark.spbu.ru/equipment-biobank-rus/equipment-biobank-genom-rus/equipment-biobank-ngsseq-rus/1762-biobank-hiseq-2500-sequencing-system-rus
  9. IBM. What are neural networks? // Retrieved from https://www.ibm.com/cloud/learn/neural-networks
  10. Mikhailov A., Pok Y.M. Artificial Neural Cortex // Smart Engineer. Syst. Design. 2001. V. 11. ASME PRESS. N. Y. P. 113-120.
  11. Mikhailov A., Karavay M. Pattern Inversion as a Pattern Recognition Method for Machine Learning // Cornell University. 2021. Retrieved from https://arxiv.org/abs/2108.10242
  12. Brin S., Page L. The Anatomy of a large-scale hypertextual web search engine // Comput. Networks ISDN Syst. 1998. V. 30. Iss. 1-7. Stanford University, Stanford, CA, 94305, USA. Retrieved from https://doi.org/10.1016/S069-7552(98)00110-X
  13. Mikhailov A. Indextron // Artificial Neural Networks in Engineering Conf. (ANNIE 1998), St. Louis, Missouri, Nov. 4-7, 1998. Proceedings Vol. 8: ANNIE 1998, Publisher: ASME Press, ISBN: 0791800822
  14. Jones K. A statistical interpretation of term specificity and its application in retrieval // J. Document.: MCB Univer.: MCB Univer. Press, 2004. V. 60. No. 5. P. 493-502. ISSN 0022-0418
  15. Sivic J., Zisserman A. Efficient visual search of videos cast as text retrieval // IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009. V. 31. Issue 4. https://doi.org/10.1109/TPAMI.2008.111
  16. UCI. Machine learning repository // Retrieved from https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq
  17. Mikhailov A., Karavay M. Indextron // Proceedings of the 10th International Conference on Pattern Recognition Application and Methods, 4-6 Feb 2021, Vienna, V.1-978-989-758-486-2. P. 143-149. https://doi.org/10.5220/0010180301430149

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