Biologically Inspired Classifier

  • Francesca Di Patti
  • Franco Bagnoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5151)

Abstract

We present a method for measuring the distance among records based on the correlations of data stored in the corresponding database entries. The original method (F. Bagnoli, A. Berrones and F. Franci. Physica A 332 (2004) 509-518) was formulated in the context of opinion formation. The opinions expressed over a set of topic originate a “knowledge network” among individuals, where two individuals are nearer the more similar their expressed opinions are. Assuming that individuals’ opinions are stored in a database, the authors show that it is possible to anticipate an opinion using the correlations in the database. This corresponds to approximating the overlap between the tastes of two individuals with the correlations of their expressed opinions.

In this paper we extend this model to nonlinear matching functions, inspired by biological problems such as microarray (probe-sample pairing). We investigate numerically the error between the correlation and the overlap matrix for eight sequences of reference with random probes. Results show that this method is particularly robust for detecting similarities in the presence of traslocations.

Keywords

knowledge network microarray 

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References

  1. 1.
    Bagnoli, F., Berrones, A., Franci, F.: De gustibus disputandum (forecasting opinions by knowledge networks). Physica A 332, 509–518 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.: Missing value estimation methods for dna microarrays. Bioinformatics 17, 520–525 (2001)CrossRefGoogle Scholar
  3. 3.
    Esponda, F., Ackley, E.S., Helman, P., Jia, H., Forrest, S.: Protecting data privacy through hard-to-reverse negative databases. In: Katsikas, S.K., López, J., Backes, M., Gritzalis, S., Preneel, B. (eds.) ISC 2006. LNCS, vol. 4176, pp. 72–84. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Maslov, S., Zhang, Y.C.: Extracting hidden information from knowledge networks. Physical Review Letters 87, 248701 (2001)CrossRefGoogle Scholar
  5. 5.
    Gan, X., Liew, A.W.C., Yan, H.: Microarray missing data imputation based on a set theoretic framework and biological knowledge. Nucleic Acids Research 34, 1608–1619 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Francesca Di Patti
    • 1
  • Franco Bagnoli
    • 1
  1. 1.Dipartimento di EnergeticaUniversità degli Studi di Firenze, Also CSDC and INFN, Sez. FirenzeFirenzeItaly

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