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

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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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ACKNOWLEDGMENTS

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

Author information

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

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The authors declare that they have no conflicts of interest.

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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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Keywords:

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