Bayesian Inference on Hidden Knowledge in High-Throughput Molecular Biology Data

  • Viet-Anh Nguyen
  • Zdena Koukolíková-Nicola
  • Franco Bagnoli
  • Pietro Lió
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5351)

Abstract

Along with the information overload brought about by the Internet in communication, economics, and sociology, high-throughput biology techniques produce vast amount of data, which are usually represented in form of matrices and considered as knowledge networks. A spectral based approach has been proved useful in extracting hidden information within such networks to estimate missing data. In this paper, we propose the use of a simple nonparametric Bayesian model to fully automate this approach and better utilize the available data at each stage of the learning process. Although the algorithm is developed with a general purpose in mind, within the scope of this paper, we evaluate its performance by applying on three different examples from the field of proteomics and genetic networks. The comparison with other general or data-specific methods has shown favor to ours. Systematic tests on synthetic data are also performed, showing the approach’s robustness in handling large percentage of missing data both in term of prediction accuracy and convergence rate. Finally, we describe a procedure to explore the nature of different types of noise containing within investigated systems.

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References

  1. 1.
    Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 (2004)CrossRefGoogle Scholar
  2. 2.
    Bagnoli, F., Berrones, A., Franci, F.: De gustibus disputandum (forecasting opinions by knowledge networks). Physica A 332, 509–518 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Comas, I., Moya, A., Gonzáez-Candelas, F.: Phylogenetic signal and functional categories in Proteobacteria genomes. BMC Evolutionary Biology 7(1), S7 (2007)Google Scholar
  4. 4.
    Everson, R., Roberts, S.: Inferring the eigenvalues of covariance matrices from limited, noisy data. IEEE Trans Signal Processing 48, 2083–2091 (2000)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Frankenstein, Z., Alon, U., Cohen, I.: The immune-body cytokine network defines a social architecture of cell interactions. Biology Direct 1(32), 1–15 (2006)Google Scholar
  6. 6.
    Gilks, W.R., Audit, B., de Angelis, D., Tsoka, S., Ouzounis, C.A.: Percolation of annotation errors through hierarchically structured protein sequence databases. Math. Biosci. 193(2), 223–234 (2005)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Assareh, A., Moradi, M.H., Volkert, L.G.: A hybrid random subspace classifier fusion approach for protein mass spectra classification. In: Marchiori, E., Moore, J.H. (eds.) EvoBIO 2008. LNCS, vol. 4973, pp. 1–11. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Macchiarulo, A., Nobeli, I., Thornton, J.: Ligand selectivity and competition between enzymes in silico. Nature Biotechnology 22(8), 1039–1045 (2004)CrossRefGoogle Scholar
  9. 9.
    Maslov, S., Zhang, Y.-C.: Extracting Hidden Information from Knowledge Networks. Physical Review Letters 87(24), 1–4 (2001)CrossRefGoogle Scholar
  10. 10.
    Minka, T.: Automatic choice of dimensionality for PCA. Neural Information Processing Systems 13 (2000)Google Scholar
  11. 11.
    Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., Ishii, S.: A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19(16), 2088–2096 (2003)CrossRefGoogle Scholar
  12. 12.
    Rajan, J.J., Rayner, P.J.W.: Model order selection for the singular value decomposition and the discrete Karhunen-Loeve transform using a Bayesian approach. IEE Vison, Image and Signal Processing 144, 123–166 (1997)Google Scholar
  13. 13.
    Smarkets is a Web-based, person-to-person betting exchange for Amazon Products, http://www.midasoracle.org/2008/03/28/smarkets/
  14. 14.
    Spellman, R., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., Futcher, B.: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)CrossRefGoogle Scholar
  15. 15.
    Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3), 611–622 (1999)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    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(6), 520–525 (2001)CrossRefGoogle Scholar
  17. 17.
    Wong, D.S.V., Wong, F.K., Wood, G.R.: A multi-stage approach to clustering and imputation of gene expression profiles. Bioinformatics 23(8), 998–1005 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Viet-Anh Nguyen
    • 1
  • Zdena Koukolíková-Nicola
    • 2
  • Franco Bagnoli
    • 3
  • Pietro Lió
    • 1
  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.Fachhochschule Nordwestschweiz, Hochschule für TechnikWindischSwitzerland
  3. 3.Department of EnergyUniversity of Florence, S. Marta, 3 50139 Firenze. Also CSDC and INFN, sez. FirenzeItaly

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