Skip to main content
SpringerLink
Log in

Aspects for implementation of data mining in gerontology and geriatrics

  • Published:
Advances in Gerontology Aims and scope Submit manuscript

Abstract

Current challenges facing the theory and practice in aging sciences require the use and development of new methods of investigation of observational and experimental data. This is associated with both the extensive development of the measuring and experimental basis of biological research and the progress in information support of studies in aging. As a result, large databases containing information on the state of health of vast groups of people who survived to an advanced age have been created. The combination of achievements in these directions make it possible to apply data mining methods that are successfully used for solving intricate tasks in economics, medical diagnostics, organization of the Internet, and other fields of science and technology for solving tasks in gerontology and geriatrics. This review provides some examples of the use of data mining methods in gerontology.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Anisimov, V.N., Molekulyarnye i fiziologicheskie mekhanizmy stareniya (Molecular and Physiological Mechanisms of Aging), St. Petersburg: Nauka, 2008.

    Google Scholar 

  2. Michalski, A.I. and Yashin, A.I., Biological control and lifespan, Probl. Uprav., 2003, vol. 3, pp. 61–65.

    Google Scholar 

  3. Michalski, A.I., Rodionov, Yu.A., Manton, K.G., et al., Frequency of disability among elderly man and women, Usp. Gerontol., 2009, vol. 22, no. 4, pp. 569–587.

    Google Scholar 

  4. Novosel’tsev, V.N. and Michalski, A.I., Mathematical modeling and aging: a research program, Usp. Gerontol., 2009, vol. 22, no. 1, pp. 117–128.

    Google Scholar 

  5. Nosko, V.P., Ekonometrika. Elementarnye metody i vvedenie v regressionnyi analiz vremennykh ryadov (Econometrics: Elementary Models and Introduction to Regression Analysis of Time Series), Moscow: Inst. Ekon. Perekh. Perioda, 2004.

    Google Scholar 

  6. Tsurko, V.V. and Michalski, A.I., Statistical analysis of relationship cancer and associated diseases, Usp. Gerontol., 2013, vol. 26, no. 4, pp. 766–774.

    CAS  Google Scholar 

  7. Azzalini, A. and Scarpa, B., Data Analysis and Data Mining: An Introduction, Oxford Univ. Press, 2012.

    Google Scholar 

  8. Barabasi, A.L., Gulbahce, N., and Loscalzo, J., Network medicine: a network-based approach to human disease, Nat. Rev. Genet., 2011, vol. 12, pp. 56–68.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Budovskaya, Y.V., Wu, K., Southworth, L.K., et al., An elt-3/elt-5/elt-6 GATA transcription circuit guides aging in C. elegans, Cell, 2008, vol. 134, pp. 291–303.

    Article  CAS  PubMed  Google Scholar 

  10. Dato, S., Soerensen, M., Lagani, V., et al., Contribution of genetic polymorphisms on functional status at very old age: a gene-based analysis of 38 genes (311 SNPs) in the oxidative stress pathway, Exp. Gerontol., 2014, vol. 52, pp. 23–29.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Li, Q., Yu, K., Li, Z., et al., MAX-rank: a simple and robust genome-wide scan for case-control association studies, Hum. Genet., 2008, vol. 123, pp. 617–623.

    Article  CAS  PubMed  Google Scholar 

  12. Managbanag, J.R., Witten, T.M., Bonchev, D., et al., Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity, PLoS One, 2008, vol. 3, p. e3802.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Michalski, A., Johnson, T., Cypser, J., et al., Heating stress patterns in Caenorhabditis elegans longevity and survivorship, Biogerontology, 2001, vol. 2, pp. 35–44.

    Article  CAS  PubMed  Google Scholar 

  14. Myasnikova, E., Samsonova, A., Samsonova, M., et al., Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns, Bioinformatics, 2002, vol. 18, suppl. 1, pp. 87–95.

    Article  Google Scholar 

  15. Sarup, P., Sorensen, P., and Loeschcke, V., The long-term effects of a life-prolonging heat treatment on the Drosophila melanogaster transcriptome suggest that heat shock proteins extend lifespan, Exp. Gerontol., 2014, vol. 50, pp. 34–39.

    Article  CAS  PubMed  Google Scholar 

  16. Shamir, L., Wolkow, C.A., and Goldberg, I.G., Quantitative measurement of aging using image texture entropy, Bioinformatics, 2009, vol. 25, pp. 3060–3063.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Swindell, W.R., Accelerated failure time models provide a useful statistical framework for aging research, Exp. Gerontol., 2009, vol. 44, pp. 190–200.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Swindell, W.R., Harper, J.M., and Miller, R.A., How long will my mouse live? Machine learning approaches for prediction of mouse lifespan, J. Gerontol., Ser. A, 2008, vol. 63, pp. 895–906.

    Article  Google Scholar 

  19. Tafaro, L., Cicconetti, P., Piccirillo, G., et al., Is it possible to predict one-year survival in centenarians? A neural network study, Gerontology, 2005, vol. 51, pp. 199–205.

    Article  PubMed  Google Scholar 

  20. Yashin, A., Cypser, J., Johnson, T., et al., Heat shock changes the heterogeneity distribution in populations of Caenorhabditis elegans: Does it tell us anything about the biological mechanism of stress response?, J. Gerontol., Ser. B, 2002, vol. 57, pp. 83–92.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Control Sciences, Russian Academy of Sciences, ul. Profsoyuznaya 65, Moscow, 117997, Russia

    A. I. Michalski

Authors
  1. A. I. Michalski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. I. Michalski.

Additional information

Original Russian Text © A.I. Michalski, 2014, published in Uspekhi Gerontologii, 2014, Vol. 27, No. 2, pp. 321–327.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Michalski, A.I. Aspects for implementation of data mining in gerontology and geriatrics. Adv Gerontol 4, 299–304 (2014). https://doi.org/10.1134/S207905701404016X

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S207905701404016X

Keywords

Search

Navigation