Abstract
An important problem in the bioinformatics field is the inference of gene regulatory networks (GRN) from temporal expression profiles. In general, the main limitations faced by GRN inference methods is the small number of samples with huge dimensionalities and the noisy nature of the expression measurements. In face of these limitations, alternatives are needed to get better accuracy on the GRNs inference problem. In this context, this work addresses this problem by presenting an alternative feature selection method that applies prior knowledge on its search strategy, called SFFS-MR. The proposed search strategy is based on SFFS algorithm, with the inclusion of multiple roots at the beginning of the search, which are defined by the best and worst single results of the SFS algorithm. In this way, the search space traversed is guided by these roots in order to find the predictor genes for a given target gene, specially to better identify genes presenting intrinsically multivariate prediction, without worsening the asymptotical computational cost of the SFFS. Experimental results show that the SFFS-MR provides a better inference accuracy than SFS and SFFS, maintaining a similar robustness of the SFS and SFFS methods. In addition, the SFFS-MR was able to achieve 60% of accuracy on network recovery after only 20 observations from a state space of size 220, thus presenting very good results.
Chapter PDF
Similar content being viewed by others
Keywords
References
Anastassiou, D.: Computational analysis of the synergy among multiple interacting genes. Molecular Systems Biology 3(83) (2007)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Barrera, J., Cesar Jr., R.M., Martins Jr., D.C., Vencio, R.Z.N., Merino, E.F., Yamamoto, M.M., Leonardi, F.G., Pereira, C.A.B., Portillo, H.A.: Methods of Microarray Data Analysis V. In: Constructing Probabilistic Genetic Networks of Plasmodium Falciparum, from Dynamical Expression Signals of the Intraerythrocytic Development Cycle, pp. 11–26. Springer, Heidelberg (2007)
Butte, A., Kohane, I.: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 418–429 (2000)
D’haeseleer, P., Liang, S., Somogyi, R.: Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8), 707–726 (2000)
Dougherty, E.R.: Validation of inference procedures for gene regulatory networks. Current Genomics 8(6), 351–359 (2007)
Dougherty, E.R., Brun, M., Trent, J.M., Bittner, M.L.: Conditioning-Based Modeling of Contextual Genomic Regulation. IEEE/ACM TCBB 6(2), 310–320 (2009), http://doi.ieeecomputersociety.org/10.1109/TCBB.2007.70247
Dougherty, E.R., Kim, S., Chen, Y.: Coefficient of determination in nonlinear signal processing. Signal Processing 80, 2219–2235 (2000)
Dougherty, J., Tabus, I., Astola, J.: Inference of gene regulatory networks based on a universal minimum description length. EURASIP Journal on Bioinformatics and Systems Biology, 1–11 (2008)
Erdös, P., Rényi, A.: On random graphs. Publ. Math. Debrecen 6, 290–297 (1959)
Faith, J., Hayete, B., Thaden, J., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J., Gardner, T.: Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology 5(1), 259–265 (2007)
Ghaffari, N., Ivanov, I., Qian, X., Dougherty, E.R.: A CoD-based reduction algorithm for designing stationary control policies on Boolean networks. Bioinformatics 26(12), 1556–1563 (2010) doi: 10.1093/bioinformatics/btq225, http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/12/1556
Hashimoto, R.F., Kim, S., Shmulevich, I., Zhang, W., Bittner, M.L., Dougherty, E.R.: Growing genetic regulatory networks from seed genes. Bioinformatics 20(8), 1241–1247 (2004)
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., Guthke, R.: Gene regulatory network inference: Data integration in dynamic models - A review. Biosystems 96(1), 86–103 (2009)
Hovatta, I., Kimppa, K., Lehmussola, A., Pasanen, T., Saarela, J., Saarikko, I., Saharinen, J., Tiikkainen, P., Toivanen, T., Tolvanen, M., et al.: DNA microarray data analysis. In: CSC, 2nd edn., Scientific Computing Ltd. (2005)
de Jong, H.: Modeling and simulation of genetic regulatory systems: A literature review. Journal of Computational Biology 9(1), 67–103 (2002)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770–780 (2008)
Liang, S., Fuhrman, S., Somogyi, R.: Reveal: a general reverse engineering algorithm for inference of genetic network architectures. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 18–29 (1998)
Lopes, F.M., Martins Jr., D.C., Cesar Jr., R.M.: Feature selection environment for genomic applications. BMC Bioinformatics 9(1), 451 (2008)
Lopes, F.M., Cesar Jr., R.M., Costa, L.d.F.: AGN simulation and validation model. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds.) BSB 2008. LNCS (LNBI), vol. 5167, pp. 169–173. Springer, Heidelberg (2008)
Margolin, A., Basso, K.N., Wiggins, C., Stolovitzky, G., Favera, R., Califano, A.: ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(suppl. 1), S7 (2006)
Martins Jr., D.C., Braga-Neto, U., Hashimoto, R.F., Dougherty, E.R., Bittner, M.L.: Intrinsically multivariate predictive genes. IEEE Journal of Selected Topics in Signal Processing 2(3), 424–439 (2008)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE TPAMI 27(8), 1226–1238 (2005)
Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature-selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)
Steuer, R., Kurths, J., Daub, C., Weise, J., Selbig, J.: The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18(Suppl. 2), 231–240 (2002)
Rao, A., Hero III, A., States, D., Engel, J.: Using directed information to build biologically relevant influence networks. In: Proc. LSS Comput. Syst. Bioinform, pp. 145–156 (August 2007)
Ris, M., Martins Jr., D.C., Barrera, J.: U-curve: A branch-and-bound optimization algorithm for u-shaped cost functions on boolean lattices applied to the feature selection problem. Pattern Recognition 43(3), 557–568 (2010)
Schllit, T., Brazma, A.: Current approaches to gene regulatory network modelling. BMC Bioinformatics 8(suppl. 6), S9 (2007)
Shalon, D., Smith, S.J., Brown, P.O.: A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research 6(7), 639–645 (1996)
Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2), 261–274 (2002)
Somol, P., Pudil, P., Kittler, J.: Fast branch & bound algorithms for optimal feature selection. IEEE TPAMI 26(7), 900–912 (2004)
Somol, P., Pudil, P., Novovičová, J., Paclík, P.: Adaptive floating search methods in feature selection. Pattern Recognition Letters 20, 1157–1163 (1999)
Stuart, J.M., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643), 249–255 (2003)
Styczynski, M.P., Stephanopoulos, G.: Overview of computational methods for the inference of gene regulatory networks. Computers & Chemical Engineering 29(3), 519–534 (2005)
Velculescu, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W.: Serial Analysis of Gene Expression. Science 270(5235), 484–487 (1995)
Wang, Z., Gerstein, M., Snyder, M.: RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10(1), 57–63 (2009)
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)
Zhao, W., Serpedin, E., Dougherty, E.R.: Inferring connectivity of genetic regulatory networks using information-theoretic criteria. IEEE/ACM TCBB 5(2), 262–274 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lopes, F.M., Martins, D.C., Barrera, J., Cesar, R.M. (2010). SFFS-MR: A Floating Search Strategy for GRNs Inference. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2010. Lecture Notes in Computer Science(), vol 6282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16001-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-16001-1_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16000-4
Online ISBN: 978-3-642-16001-1
eBook Packages: Computer ScienceComputer Science (R0)
