Intellectual Mining of Patient Data with Melanoma for Identification of Disease Markers and Critical Genes

  • 2 Accesses


Genotypic (DNA mutations) and phenotyping data on patients with melanoma are analyzed to identify markers of early disease diagnosis and critical involved genes. An optimal mining method was chosen from those that are traditionally used in the field. This method allows one to analyze a set of terms. Automatic and interactive approaches were performed, which both allow a considerable reduction in the computational requirements. New melanoma-associated genes and candidate relapse markers were identified. Data mining was performed with the JSM method of automated support of scientific research (JSM ASSR).

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA


  1. 1

    Zaridze, D.G., Kantserogenez (Carcinogenesis), Moscow: Meditsina, 2004.

  2. 2

    Anshakov, O.M. and Fabrikantova, E.F., 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.

  3. 3

    Finn, V.K. and Shesternikova, O.P., The heuristics of detection of empirical regularities by JSM reasoning, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 5, pp. 215–247.

  4. 4

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

  5. 5

    Shesternikova, O.P., Agafonov, M.A., Vinokurova, L.V., Pankratova, E.S., and Finn, V.K., Intelligent system for diabetes prediction in patients with chronic pancreatitis, Sci. Tech. Inf. Process., 2016, vol. 43, nos. 5–6, pp. 315–345.

  6. 6

    Birkhoff, G., Lattice Theory, American Mathematial Society, 1948.

  7. 7

    Zabezhailo, M.I., The approximate JSM method with examples, Nauchno-Tekh. Inf., Ser. 2, 2014, no. 10, pp. 1–12.

  8. 8

    Ganter, B. and Wille, R., Formal Concept Analysis: Mathematical Foundations, Berlin: Springer, 1999.

  9. 9

    Gao, J., Aksoy, B.A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S.O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., Cerami, E., Sander, C., and Schultz, N., Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal, Sci. Signaling, 2013, vol. 6, no. 269, p. 11.

  10. 10

    Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Jacobsen, A., Byrne, C.J., Heuer, M.L., Larsson, E., Antipin, Y., Reva, B., Goldberg, A.P., Sander, C., and Schultz, N., The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data, Cancer Discovery, 2012, vol. 2, no. 5, pp. 401–404.

  11. 11

    Forbes, S.A., Beare, D., Boutselakis, H., Bamford, S., Bindal, N., Tate, J., Cole, C.G., Ward, S., Dawson, E., and Ponting, L., COSMIC: Somatic cancer genetics at high-resolution, Nucleic Acids Res., 2017, vol. 45, no. d1, pp. D777–D783.

Download references


The authors are grateful to Mikhail I. Zabezhailo for valuable recommendations and ideas.

Author information

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

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Translated by L. Rusin

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chebanov, D.K., Mikhailova, I.N. Intellectual Mining of Patient Data with Melanoma for Identification of Disease Markers and Critical Genes. Autom. Doc. Math. Linguist. 53, 283–287 (2019).

Download citation


  • artificial intelligence
  • oncology
  • genotypic data
  • phenotypic data
  • mutations
  • JSM ASSR method