Feature Evaluation Metrics for Population Genomic Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8445)


Single Nucleotide Polymorphisms (SNPs) are considered nowadays one of the most important class of genetic markers with a wide range of applications with both scientific and economic interests. Although the advance of biotechnology has made feasible the production of genome wide SNP datasets, the cost of the production is still high. The transformation of the initial dataset into a smaller one with the same genetic information is a crucial task and it is performed through feature selection. Biologists evaluate features using methods originating from the field of population genetics. Although several studies have been performed in order to compare the existing biological methods, there is a lack of comparison between methods originating from the biology field with others originating from the machine learning. In this study we present some early results which support that biological methods perform slightly better than machine learning methods.


Feature selection Single nucleotide polymorphism SNPs Bioinformatics Machine learning 


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  1. 1.
    Wilkinson, S., Wiener, P., Archibald, A., et al.: Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC Genet. 12, 45 (2011)CrossRefGoogle Scholar
  2. 2.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach Learn Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  3. 3.
    Nielsen, E., Cariani, A., Mac Aoidh, E., et al.: Gene-associated markers provide tools for tackling illegal fishing and false eco-certification. Nat. Com. 3, 851 (2012), doi:10.1038/ncomms1845Google Scholar
  4. 4.
    Wilkinson, S., Archibald, A., Haley, C., et al.: Development of a genetic tool for product regulation in the diverse British pig breed market. BMC Gen. 13, 580 (2012)CrossRefGoogle Scholar
  5. 5.
    Piry, S., Alapetite, A., Cornuet, J.M., Petkau, D., Baudouin, L., Estoup, A.: GENECLASS2: A software for genetic assignment and first generation migrant detection. J. Hered. 95, 536–539 (2004)CrossRefGoogle Scholar
  6. 6.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)Google Scholar
  7. 7.
    Shriver, M.D., Smith, M.W., Jin, L., et al.: Ethnic affiliation estimation by use of population-specific DNA markers. Am. J Hum. Genet. 60, 957–964 (1997)Google Scholar
  8. 8.
    Wright, S.: The genetical structure of populations. Ann Eugenic 15, 323 (1951)CrossRefGoogle Scholar
  9. 9.
    Beebee, T., Rowe, G.: An Introduction to Molecular Ecology. Oxford University Press, Oxford (2004)Google Scholar
  10. 10.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11, 10–18 (2009)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., et al.: Gene selection from microarray data for cancer classification–a machine learning approach. Comput. Biol. Chem. 29, 37–46 (2005)CrossRefzbMATHGoogle Scholar
  12. 12.
    Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of relief and relieff. Mach. Lean. 53, 23–69 (2003)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceAristotle University of ThessalonikiGreece
  2. 2.Department of Genetics, Development and Molecular Biology, School of BiologyAristotle University of ThessalonikiGreece

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