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Molecular and Cellular Biochemistry

, Volume 452, Issue 1–2, pp 133–140 | Cite as

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

  • Alla P. ToropovaEmail author
  • Andrey A. Toropov
  • Aleksandar M. Veselinović
  • Jovana B. Veselinović
  • Danuta Leszczynska
  • Jerzy Leszczynski
Article
  • 71 Downloads

Abstract

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as “correlations along two parallel lines.” This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: “inactive/active” (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews’s correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

Keywords

Semi-correlation SAR Monte Carlo method Mutagenicity Ames test Index of ideality of correlation 

Notes

Acknowledgements

APT and AAT are grateful for the contribution of the EU project LIFE-COMBASE (LIFE15 ENV/ES/000416). DL and JL were supported by the NSF CREST Interdisciplinary Nanotoxicity Center Grant # HRD-1547754.

Author Contributions

Authors have done equivalent contributions to this work.

Compliance with ethical standards

Conflict of interest

The authors confirm that this article content has no conflict of interest.

Supplementary material

11010_2018_3419_MOESM1_ESM.xlsx (346 kb)
Supplementary material 1 Supplementary materials section contains technical details of the best model: (i) distribution into the training, invisible training, calibration, and validation sets; (ii) observed and predicted values of the categorical endpoint; (iii) domain of applicability; and (iv) correlation weights used to calculated the DCW(T*, N*). (XLSX 346 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Alla P. Toropova
    • 1
    Email author
  • Andrey A. Toropov
    • 1
  • Aleksandar M. Veselinović
    • 2
  • Jovana B. Veselinović
    • 2
  • Danuta Leszczynska
    • 3
  • Jerzy Leszczynski
    • 4
  1. 1.Laboratory of Environmental Chemistry and ToxicologyIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanoItaly
  2. 2.Department of Chemistry, Faculty of MedicineUniversity of NišNišSerbia
  3. 3.Department of Civil and Environmental Engineering, Interdisciplinary Nanotoxicity CenterJackson State UniversityJacksonUSA
  4. 4.Department of Chemistry and Biochemistry, Interdisciplinary Nanotoxicity CenterJackson State UniversityJacksonUSA

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