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On the Methods of Artificial Intelligence for Analysis of Oncological Data

Abstract

A brief overview of artificial intelligence techniques applied to medical data related to oncology is provided. The actual goals of using artificial intelligence are listed, that is, the types of applied problems solved with its use. The initial information is described, which, as a rule, contains genotypic data: about DNA and associated molecules, as well as the general clinical parameters of patients. The description of the logical-mathematical and software approaches of the most known solutions in this area is given. This work is intended to familiarize data analysts with the challenges in modern oncology with the use of artificial intelligence, as well as to guide biomedical researchers on the variety of data-mining methods and capabilities.

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REFERENCES

  1. 1

    Nindrea, R.D., Aryandono, T., Lazuardi, L., and Dwiprahasto, I., Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: A meta-analysis, Asian Pac. J. Cancer Prev., 2018, vol. 19, no. 7, pp. 1747–1752.

    Google Scholar 

  2. 2

    Xie, G., Dong, C., Kong, Y., Zhong, J.F., Li, M., and Wang, K., Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features, Genes (Basel), 2019, vol. 10, no. 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471789/.

  3. 3

    Chen, H., Kodell, R.L., Cheng, K.F., et al., Assessment of performance of survival prediction models for cancer prognosis, BMC Med. Res. Methodol., 2012, vol. 12, p. 102. https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-12-102.

    Article  Google Scholar 

  4. 4

    Su, J., Zhang, Y., Su, H., Zhang, C., and Li, W., A recurrence model for laryngeal cancer based on SVM and gene function clustering, Acta Otolaryngol., 2017, vol. 137, no. 5, pp. 557–562.

    Article  Google Scholar 

  5. 5

    Chen, R., Garapati, S., Wu, D., Ko, S., Falk, S., Dierov, D., Stasiw, A., Opong, A.S., and Carson, K.R., Machine learning based predictive model of 5-year survival in multiple myeloma autologous transplant patients, Blood, 2019, vol. 134. https://ashpublications.org/blood/article/134/Supplement_1/2156/427904/Machine-Learning-Based-Predictive-Model-of-5-Year.

  6. 6

    Rhee, S., Seo, S., and Kim, S., Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), 2018. https://arxiv.org/abs/1711.05859.

  7. 7

    Kosinsky, Y., Dovedi, S.J., Peskov, K., Voronova, V., Chu, L., Tomkinson, H., Al-Huniti, N., Stanski, D.R., and Helmlinger, G., Radiation and PD-(L)1 treatment combinations: Immune response and dose optimization via a predictive systems model, J. Immunother. Cancer, 2018, no. 1, pp. 6–17.

  8. 8

    Voit, E.O., Modelling metabolic networks using power-laws and S-systems, Essays Biochem., 2008, vol. 45, pp. 29–40.

    Article  Google Scholar 

  9. 9

    Yanardag, P. and Vishwanathan, S.V.N., Deep graph kernels, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. https://dl.acm.org/doi/10.1145/2783258.2783417.

  10. 10

    Li, H., Gong, X., Yu, H., and Zhou, C., Deep neural network based predictions of protein interactions using primary sequences, Molecules, 2018, vol. 23. https://www.researchgate.net/publication/326755315_Deep_Neural_Network_Based_Predictions_of_Protein_Interactions_Using_Primary_Sequences.

  11. 11

    Way, G.P., Sanchez-Vega, F., La, K., Armenia, J., Chatila, W.K., Luna, A., Sander, A., Cherniack, A.D., Mina, M., Ciriello, G., and Schultz, N., Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas, Cell Rep., 2018, vol. 23, no. 1, pp. 172–180.

    Article  Google Scholar 

  12. 12

    Ambwani, G., Cohen, A., Estévez, M., Singh, N., Adamson, B., Nussbaum, N.C., and Birnbaum, B., A machine learning model for cancer biomarker identification in electronic health records, Value Health, 2019, vol. 22, no. S1.

  13. 13

    Intelligent System for Predicting Cancer Tempus (USA). https://www.tempus.com.

  14. 14

    Intelligent System for Predicting Cancer FlatIron (USA). https://flatiron.com/.

  15. 15

    Deist, T.M., Dankers, F.J.W.M., Valdes, G., Wijsman, R., Hsu, I.C., Oberije, C., Lustberg, T., Van Soest, J., Hoebers, F., Jochems, A., Naqa, I.El., Wee, L., Morin, O., Raleigh, D.R., Bots, W., et al., Machine learning algorithms for outcome prediction in (chemo) radiotherapy: An empirical comparison of classifiers, Med. Phys., 2018, vol. 45, no. 7, pp. 3449–3459.

    Article  Google Scholar 

  16. 16

    Enshaei, A., Robson, C.N., and Edmondson, R.J., Artificial intelligence systems as prognostic and predictive tools in ovarian cancer, Ann. Surg., 2015, vol. 22, no. 12, pp. 3970–3975.

    Article  Google Scholar 

  17. 17

    Michuda, J., Leibowitz, B., Amar-Farkash, S., Bevis, C., Breschi, A., Kapilivsky, J., Igartua, C., Bell, J.S.K., Beauchamp, K.A., White, K., Stumpe, M., Beaubier, N., and Taxter, T., Multimodal prediction of diagnosis for cancers of unknown primary, AACR Annual Meeting 2020. Session PO.CL01.04—Tumor Type-Focused Translational Research Specific, 2020. https://www.abstractsonline.com/pp8/?sf122451697=1#!/9045/presentation/3059.

  18. 18

    Wan, N., Weinberg, D., Liu, T.Y., et al., Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA, BMC Cancer, 2019, vol. 19, no. 1. https://b-mccancer.biomedcentral.com/articles/10.1186/s12885-019-6003-8.

  19. 19

    Database The Cancer Genome Atlas Program. https://www.cancer.gov/tcga.

  20. 20

    Shesternikova, O.P., Finn, V.K., Vinokurova, L.V., Les’ko, K.A., Varvanina, G.G., and Tyulyaeva, E.Yu., An intelligent system for diagnostics of pancreatic diseases, Autom. Doc. Math. Linguist., 2019, vol. 53, no. 5, pp. 288–291.

    Article  Google Scholar 

  21. 21

    Ding, W., Chen, G., and Shi, T., Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis, Epigenetics, 2019, vol. 14, no. 1, pp. 67–80.

    Article  Google Scholar 

  22. 22

    Kanehisa, M. and Goto, S., KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 2000, vol. 28, pp. 27–30.

    Article  Google Scholar 

  23. 23

    Manica, M., Cadow, J., Mathis, R., and Martinez, M.R., PIMKL: Pathway induced multiple kernel learning, NPJ Syst. Biol. Appl., 2019, vol. 5, no. 8. https://arxiv.org/abs/1803.11274.

  24. 24

    Sanchez-Vega, F., Mina, M., Armenia, J., Ciriello, G., Sander, C., Schultz, N., et al., Oncogenic signaling pathways in the cancer genome atlas, Cell, 2018, vol. 173, no. 2, pp. 321–337.

    Article  Google Scholar 

  25. 25

    Ma, J., Ku, Yu.M., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., and Ideker, T., Using deep learning to model the hierarchical structure and function of a cell, Nat. Methods, 2018, vol. 15, pp. 290–298.

    Article  Google Scholar 

  26. 26

    Friedman, N., Linial, M., Nachman, I., and Pe’er, D., Using Bayesian networks to analyze expression data, Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, 2000. https://www.cs.huji.ac.il/~nir/Papers/FLNP1Full.pdf.

  27. 27

    Finn, V.K., On the heuristics of JSM research (additions to articles), Autom. Doc. Math. Linguist., 2019, vol. 53, no. 5, pp. 250–282.

    Article  Google Scholar 

  28. 28

    Finn, V.K., Distributive lattices of inductive JSM procedures, Autom. Doc. Math. Linguist., 2014, vol. 53, no. 5, pp. 265–296.

    Article  Google Scholar 

  29. 29

    DSM-metod avtomaticheskogo porozhdeniya gipotez: Logicheskie i epistemologicheskie osnovaniya (The JSM-Method for Automatic Hypothesis Generation: Logical and Epistemological Foundations), Anshakov, O.M., Ed., Moscow: LIBROKOM, 2009.

    Google Scholar 

  30. 30

    Finn, V.K., On the definition of empirical regularities by the JSM method for the automatic generation of hypotheses, Sci. Tech. Inf. Process., 2012, vol. 39, pp. 261–267.

    Article  Google Scholar 

  31. 31

    Finn, V.K. and Shesternikova, O.P., On a new version of the generalized JSM method of automated support for scientific research, Iskusstv. Intell. Prinyatie Reshenii, 2016, no. 1, pp. 57–64.

  32. 32

    Chebanov, D.K. and Mikhailova, I.N., Intellectual mining of patient data with melanoma for identification of disease markers and critical genes, Autom. Doc. Math. Linguist., vol. 53, no. 5, pp. 283–288.

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Funding

This study was financially supported by the Russian Foundation for Basic Research (project no. 18-29-03063).

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Correspondence to D. K. Chebanov or I. N. Mikhaylova.

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The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

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Chebanov, D.K., Mikhaylova, I.N. On the Methods of Artificial Intelligence for Analysis of Oncological Data. Autom. Doc. Math. Linguist. 54, 255–259 (2020). https://doi.org/10.3103/S0005105520050027

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Keywords:

  • artificial intelligence
  • intelligent system
  • oncology
  • genetic data
  • mutations
  • immune data
  • JSM‑method of ASSR