Artificial Intelligence in Medicine
Volume 48, Issue 2 , Pages 153-160 , February 2010

A new multiple regression approach for the construction of genetic regulatory networks

  • Shu-Qin Zhang

      Affiliations

    • School of Mathematical Sciences, Fudan University, Shanghai, China
    • ,
  • Wai-Ki Ching

      Affiliations

    • Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong
    • Corresponding Author InformationCorresponding author. Tel.: +852 2859 2256; fax: +852 2559 2225.
    • ,
  • Nam-Kiu Tsing

      Affiliations

    • Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong
    • ,
  • Ho-Yin Leung

      Affiliations

    • Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong
    • ,
  • Dianjing Guo

      Affiliations

    • Department of Biology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Received 12 August 2008 ,Revised 4 September 2009 ,Accepted 8 September 2009.

References 

  1. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3:article number 78
  2. de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol. 2002;9:67–103
  3. Akutsu T, Miyano S, Kuhara S. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics. 2000;16:727–734
  4. Boros E, Ibaraki T, Makino K. Error-free and best-fit extensions of partially defined Boolean functions. Inform Comput. 1998;140:254–273
  5. Ching W, Fung E, Ng M, Akutsu T. On construction of stochastic genetic networks based on gene expression sequences. Int J Neural Syst. 2005;15:297–310
  6. Hirose O, Nariai N, Tamada Y, Bannai H, Imoto S, Miyano S. Estimating gene networks from expression data and binding location data via Boolean networks. Lect Note Comput Sci. 2006;3480:349–356
  7. Ideker TE, Thorsson V, Karp RM. Discovery of regulatory interactions through perturbation: inference and experimental design. In:  Altman RB,  Dunker AK,  Hunter L,  Lauderdale K,  Klein TED editor. Proceedings of the Pacific Symposium Biocomputing, vol. 5. Hawaii, US, 4–9 January 2000. World Scientific Publishing Company; 2000;p. 302–313
  8. Noda K, Shinohara A, Takeda M, Matsumoto S, Miyano S, Kuhara S. Finding genetic network from experiments by weighted network model. Genome Inform. 1998;9:141–150
  9. Shmulevich I, Saarinen A, Yli-Harja O, Astola J. Inference of genetic regulatory networks under the best-fit extension paradigm. In:  Zhang W,  Shmulevich I editor. Computational and Statistical Approaches to Genomics. Boston, MA: Kluwer; 2002;p. 197–210
  10. Eisen M, Spellman P, Brown P, Botsterin D. Clustering analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998;95:14863–14868
  11. Friedman N, Elidan G. Bayesian network software libB 2.1. http://www.cs.huji.ac.il/labs/compbio/LibB/ (Accessed 20 June 2009).
  12. Yu J, Smith VA, Wang PP, Hartemink AJ, Javis ED. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004;20:3594–3603
  13. Bansal M, Giusy DG, di Bernardo D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics. 2006;22:815–822
  14. D’haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modeling of mRNA expression levels during CNS development and injury. In:  Altman RB,  Dunker AK,  Hunter L,  Lauderdale K,  Klein TED editor. Proceedings Symposium Biocomputation. Hawaii, US, 4–9 January. World Scientific Publishing Company; 1999;p. 41–52
  15. Gardner TS, di Bernardo D, Lorenz D, Collins JJ. Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 2003;301:102–105
  16. Tegner J, Yeung MK, Hasty J, Collins JJ. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA. 2003;100(10):5944–5949
  17. di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, et al. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol. 2005;23(3):377–383
  18. van Someren EP, Vaes BLT, Steegenga WT, Sijbers AM, Dechering KJ, Reinders MJ. Least absolute regression network analysis of the murine osteoblast differentiation network. Bioinformatics. 2006;22(4):477–484
  19. Barabási AL, Albert R. Emergence of scaling in random networks. Science. 1999;286:509–512
  20. Guelzim N, Bottani S, Bourgine P, Képès F. Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet. 2002;31:60–63
  21. Golub G, van Loan C. Matrix computations. Baltimore: The John Hopkins University Press; 1993;
  22. Hansen PC. Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion. Philadelphia: SIAM, Monographs on Mathematical Modeling and Computation; 1998;
  23. Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics. 2001;17:763–774
  24. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, et al. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell. 1998;2:65–73

☆ Preliminary version was presented in the International Conference on BioMedical Engineering and Informatics, 2008.

PII: S0933-3657(09)00160-2

doi: 10.1016/j.artmed.2009.11.001

Artificial Intelligence in Medicine
Volume 48, Issue 2 , Pages 153-160 , February 2010

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