Advertisement

Informative Gene Discovery in DNA Microarray Data Using Statistical Approach

  • Kentaro Fukuta
  • Yoshifumi Okada
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)

Abstract

Preventing, diagnosing, and treating disease is greatly facilitated by the availability of biomarkers. Recent improvements in bioinformatics technology have facilitated large-scale screening of DNA microarrays for candidate biomarkers. Here we discuss a gene selection method, which is called LEAve-one-out Forward selection method (LEAF), for discovering informative genes embedded in gene expression data, and propose an additional algorithm for extending LEAF’s capabilities. LEAF is an iterative forward selection method incorporating the concept of leave-one-out cross validation (LOOCV) and provides a discrimination power score (DPS) for genes, which is a criterion for selecting the candidate of informative genes. We show that LEAF identifies genes that are practically used as biomarkers. Our method should be useful bioinformatics tool for biomedical, clinical, and pharmaceutical researchers.

Keywords

Biomarkers Data mining Gene expression profiles Cancer classification 

Notes

Acknowledgements

A part of this work was supported by Promotion for Young Research Talent and Network from Northern Advancement Center for Science & Technology (NOASTEC Japan) and Grant-in-Aid for Young Scientists (B) No.21700233 from MEXT Japan.

References

  1. 1.
    Armstrong SA, Staunton JE, Silverman LB, et al (2001) Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia. BioinNature Genet 30(1):41–47CrossRefGoogle Scholar
  2. 2.
    Turck, Chris (Ed.) (2009) Biomarkers for Psychiatric Disorders. Springer, U.S.Google Scholar
  3. 3.
    Kevin Ahern, Ph.D. (2009) GEN Best of the Web. Genetic Engineering & Biotechnology News 29(8):66Google Scholar
  4. 4.
    Fukuta K, Nagashima T, Okada Y (2010) Leaf: leave-one-out forward selection method for cancer classification using gene expression data. Proceedings of The 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010, 18–20, August, pages 31–36Google Scholar
  5. 5.
    Fukuta K, Okada Y (2011) Leaf: Leave-one-out forward selection method for gene selection in dna microarray data. Lecture Notes in Engineering and ComputerScience: Proceedings of The International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011, 16–18 March, Hong Kong, pages 175–180Google Scholar
  6. 6.
    Gene Ontology Consortium (2000a) Gene ontology: tool for the unification of biology. Nature Genet 25:25–29CrossRefGoogle Scholar
  7. 7.
    Golub T-R, Slonim D-K, Tamayo P, Huard C, Gaasenbeek M, Mesirov J-P, Coller H, Loh M-L, Downing J-R, Caligiuri M-A, et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537CrossRefGoogle Scholar
  8. 8.
    Gordon GJ, Jensen RV, Hsiao LL, Gullans SR, Blumenstock JE, Ramaswamy S, Richards WG, Sugarbaker DJ, Bueno R (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 62:4963–4967Google Scholar
  9. 9.
    Lachenbruch PA (1979) Discriminant analysis. Gendai-Sugakusha, KyotoGoogle Scholar
  10. 10.
    Mitsubayashi H, Aso S, Nagashima T, Okada Y (2008) Accurate and robust gene selection for disease classification using a simple statistic. Bioinformation 3(2):68–71Google Scholar
  11. 11.
    Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D’Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR8 (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1:203–209Google Scholar
  12. 12.
    Newman JC, Weiner AM (2005) L2L: a simple tool for discovering the hidden significance in microarray expression data. Genome Biol 6(9):R81CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Satellite Venture Business LaboratoryMuroran Institute of TechnologyMuroranJapan
  2. 2.College of Information and SystemsMuroran Institute of TechnologyMuroranJapan

Personalised recommendations