Abstract
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).
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REFERENCES
Zaridze, D.G., Kantserogenez (Carcinogenesis), Moscow: Meditsina, 2004.
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.
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.
Finn, V.K., On the heuristics of JSM research (additions to articles), Autom. Doc. Math. Linguist., 2019, vol. 53, no. 5, pp. 250–282.
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.
Birkhoff, G., Lattice Theory, American Mathematial Society, 1948.
Zabezhailo, M.I., The approximate JSM method with examples, Nauchno-Tekh. Inf., Ser. 2, 2014, no. 10, pp. 1–12.
Ganter, B. and Wille, R., Formal Concept Analysis: Mathematical Foundations, Berlin: Springer, 1999.
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.
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.
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.
ACKNOWLEDGMENTS
The authors are grateful to Mikhail I. Zabezhailo for valuable recommendations and ideas.
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Translated by L. Rusin
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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). https://doi.org/10.3103/S0005105519050066
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DOI: https://doi.org/10.3103/S0005105519050066