Abstract
Determining the underlying regulatory mechanism of genetic networks is one of the central challenges of computational biology. Numerous methods have been developed and applied to the important but complex task of reverse engineering regulatory networks from high-throughput gene expression data. However, many challenges remain. In this paper, we are interested in learning rules that will reveal the causal genes for the expression variation from various relational data sources in addition to gene expression data. Following our previous work where we showed that time series gene expression data could potentially uncover causal effects, we describe an application of an inductive logic programming (ILP) system, to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism. Specifically, we learn rules for predicting gene expression levels at the next time step based on the available relational data and then generalize the learned theory to visualize a pruned network of important interactions. We evaluate and present experimental results on microarray experiments from Gasch et al on Saccharomyces cerevisiae.
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References
Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S.: Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions. In: Proc. the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 695–702. ACM Press, New York (1998)
Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P.G.K., King, R.D.: Combining inductive logic programming, active learning, and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence 6, 1–36 (2001)
Chrisman, L., Langley, P., Bay, S., Pohorille, A.: Incorporating biological knowledge into evaluation of causal regulatory hypotheses. In: Pacific Symposium on Biocomputing (PSB) (January 2003)
Ptacek, J., et al.: Global analysis of protein phosphorylation in yeast. Nature 438, 679–684 (2005)
Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7(3/4), 601–620 (2000)
Fröhler, S., Kramer, S.: Inductive logic programming for gene regulation prediction. In: Proceedings of the 16th International Conference on Inductive Logic Programming, Santiago de Compostela, Spain, pp. 83–85. University of Corunna (2006)
Gasch, A.P., Huang, M., Metzner, S., Botstein, D., Elledge, S.J., Brown, P.O.: Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p. Mol. Biol. Cell 12, 2987–3003 (2001)
Gasch, A.P., Spellman, P.T., Kao, C.M., Carmel-Harel, O., Eisen, M.B., Storz, G., Botstein, D., Brown, P.O.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000)
Güldener, U., Münsterkötter, M., Kastenmüller, G., Strack, N., van Helden, J., Lemer, C., Richelles, J., Wodak, S.J., Garcia-Martinez, J., Perez-Ortin, J.E., Michael, H., Kaps, A., Talla, E., Dujon, B., Andre, B., Souciet, J.L., De Montigny, J., Bon, E., Gaillardin, C., Mewes, H.W.: CYGD: the Comprehensive Yeast Genome Database. Nucleic Acids Research 33, D364–368 (2005)
Harrison, J.C., Haber, J.E.: Surviving the breakup: The DNA damage checkpoint. Annu. Rev. Genet. 40, 209–235 (2006)
Ideker, T.E., Thorsson, V., Karp, R.M.: Discovery of regulatory interactions through perturbation: Inference and experimental design. In: Pacific Symposium on Biocomputing, pp. 302–313 (2000)
Ptacek, J., Snyder, M.: Charging it up: global analysis of protein phosphorylation. Trends in Genetics 22, 545–554 (2006)
King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.J.K., Bryant, C.H., Muggleton, S., Kell, D.B., Oliver, S.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y., Leslie, C.: Predicting genetic regulatory response using classification. Bioinformatics 20, 232–240 (2004)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)
Ong, I.M., Glasner, J.D., Page, D.: Modelling regulatory pathways in Escherichia coli from time series expression profiles. Bioinformatics 18, S241–S248 (2002)
Papatheodorou, I., Kakas, A., Sergot, M.: Inference of gene relations from microarray data by abduction. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 389–393. Springer, Heidelberg (2005)
Pe’er, D., Regev, A., Elidan, G., Friedman, N.: Inferring subnetworks from perturbed expression profiles. In: Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology, pp. 215–224. Oxford University Press, Oxford (2001)
Pe’er, D., Regev, A., Tanay, A.: Minreg: Inferring an active regulator set. In: Proceedings of the 10th International Conference on Intelligent Systems for Molecular Biology, pp. S258–S267. Oxford University Press, Oxford (2002)
Reiser, P.G.K., King, R.D., Kell, D.B., Muggleton, S.H., Bryant, C.H., Oliver, S.G.: Developing a logical model of yeast metabolism. Electronic Transactions in Artificial Intelligence 5, 223–244 (2001)
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition specific regulators from gene expression data. Nature Genetics 34, 166–176 (2003)
Srinivasan, A.: The Aleph Manual. University of Oxford, Oxford (2001)
Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34, D535–539 (2006)
Struyf, J., Dzeroski, S., Blockeel, H., Clare, A.: Hierarchical multi-classification with predictive clustering trees in functional genomics. In: Progress in Artificial Intelligence: 12th Portugese Conference on Artificial Intelligence, pp. 272–283. Springer, Heidelberg (2005)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.H.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)
Tanay, A., Shamir, R.: Computational expansion of genetic networks. Bioinformatics, 17 (2001)
Teixeira, M.C., Monteiro, P., Jain, P., Tenreiro, S., Fernandes, A.R., Mira, N.P., Alenquer, M., Freitas, A.T., Oliveira, A.L.: The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Research 34, 446–451 (2006)
Tu, Z., Wang, L., Arbeitman, M.N., Chen, T., Sun, F.: An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics, e489–e496 (2006)
Zien, A., Kuffner, R., Zimmer, R., Lengauer, T.: Analysis of gene expression data with pathway scores. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 407–417 (2000)
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Ong, I.M., Topper, S.E., Page, D., Costa, V.S. (2007). Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_34
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DOI: https://doi.org/10.1007/978-3-540-73847-3_34
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