Abstract
The complex regulatory mechanisms of genes and their transcription are the major gene regulatory steps in the cell. Gene Regulatory Networks (GRNs) and DNA Microarrays (MAs) present two of the most prominent and heavily researched concepts in contemporary molecular biology and bioinformatics. The challenge in contemporary biomedical informatics research lies in systems biology - the linking of various pillars of heterogeneous data so they can be used in synergy for life science research. Faced with this challenge we devised and present an integrated methodology that ‘amalgamates’ knowledge and data from both GRNs and MA gene expression sources. The methodology, is able to identify phenotype-specific GRN functional paths, and aims to uncover potential gene-regulatory ‘fingerprints’ and molecular mechanisms that govern the genomic profiles of diseases. Initial implementation and experimental results on a real-world breast-cancer study demonstrate the suitability and reliability of the approach.
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References
Ideker, T., Galitski, T., Hood, L.: A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001)
Collins, F.S., Green, E.D., Guttmacher, A.E., Guyer, M.S.: ‘A Vision for the Future of Genomics Research. Nature 422(6934), 835–847 (2003)
Kumar, A.: Teaching Systems Biology: An Active-learning Approach. Cell Biol. Educ. 4(4), 323–329 (2005)
Friend, H.F.: How DNA microarrays and expression profiling will affect clinical practice. Br. Med. J. 319, 1–2 (1999)
Bassett, D.E., Eisen, M.B., Boguski, M.S.: Gene expression informatics: it’s all in your mine. Nature Genetics 21(suppl. 1), 51–55 (1999)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
van ’t Veer, L.J., et al.: Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer. Nature 415, 530–536 (2002)
Potamias, G., Koumakis, L., Moustakis, V.: Gene Selection via Discretized Gene-Expression Profiles and Greedy Feature-Elimination. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 256–266. Springer, Heidelberg (2004)
Bower, J.M., Bolouri, H.: Computational Modeling of Genetic and Biochemical Networks. Computational Molecular Biology Series. MIT Press, Cambridge (2001)
Arkin, Ross, J.: Computational functions in biochemical reaction networks. Biophys J. 67(2), 560–578 (1994)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford Univ. Press, New York (1993)
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pac. Symp. Biocomput., pp. 17–28 (1999)
Imoto, S., Goto, T., Miyano, S.: Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In: Pac. Symp. Biocomput., pp. 175–186 (2002)
Kimura, S., et al.: Inference of S-system models of genetic networks using a cooperative co-evolutionary algorithm. Bioinformatics 21(7), 1154–1163 (2005)
Segal, E., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34(2), 166 (2003)
Guanrao, P.L., Almasri, E., Dai, Y.: Rank-based edge reconstruction for scale-free genetic regulatory networks. BMC Bioinformatics 9, 75 (2008)
Daisuke, T., Horton, P.: Inference of scale-free networks from gene expression time series. Journal of Bioinformatics and Computational Biology 4(2), 503–514 (2006)
Milenkovic, T., Lai, J., Przulj, N.: GraphCrunch: a tool for large network analyses. BMC Bioinformatics 9, 70 (2008)
Wu, C.C., Huang, H.C., Juan, H.F., Chen, S.T.: GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data. Bioinformatics 20(18), 3691–3693 (2004)
Simon, R., Radmacher, M.D., Dobbin, K., McShane, L.M.: Pitfalls in the Use of DNA Microarray Data for Diagnostic Classification. Journal of the National Cancer Institute 95(1), 14–18 (2003)
Pan, W.: A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18(4), 546–554 (2002)
Kitano, H.: Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)
Kwoh, K., Ng, P.Y.: ‘Network analysis approach for biology. Cell. Mol. Life Sci. 64, 1739–1751 (2007)
Anisimov, S.V., Christophersen, N.S., Correia, A.S., Li, J.-Y., Brundin, P.: NeuroStem Chip: a novel highly specialized tool to study neural differentiation pathways in human stem cells. BMC Genomics 8, 46 (2007)
Doniger, S.W., Salomonis, N., Dahlquist, K.D., Vranizan, K., Lawlor, S.C., Conklin, B.R.: MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biology 4(1), R7 (2003)
Mlecnik, B., Scheideler, M., Hackl, H., Hartler, J., Sanchez-Cabo, F., Trajanoski, Z.: PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res. 33, W633-W637 (2005)
Kulterer, B., et al.: Gene expression profiling of human mesenchymal stem cells derived from bone marrow during expansion and osteoblast differentiation. BMC Genomics 8, 70 (2007)
Weniger, M., Engelmann, J.C., Schultz, J.: Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context. BMC Bioinformatics 8, 179 (2007)
Grosu, P., Townsend, J.P., Hartl, D.L., Cavalieri, D.: Pathway Processor: A Tool for Integrating Whole-Genome Expression Results into Metabolic Networks. Genome Research 12(7), 1121–1126 (2002)
Evans, W.E., Relling, M.V.: Moving towards individualized medicine with pharmacogenomics. Nature 429, 464–468 (2004)
Kafetzopoulos, D.: The Prognochip Project: Transcripromics and Biomedical Informatics for the Classification and Prognosis of Breast Cancer. ERCIM News 60 (2005)
Analyti, A., Kondylakis, H., Manakanatas, D., Kalaitzakis, M., Plexousakis, D., Potamias, G.: Integrating Clinical and Genomic Information through the PrognoChip Mediator. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds.) ISBMDA 2006. LNCS (LNBI), vol. 4345, pp. 250–261. Springer, Heidelberg (2006)
Potamias, G., Kanterakis, A.: Supporting Clinico-Genomic Knowledge Discovery: A Multi-strategy Data Mining Process. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) SETN 2006. LNCS (LNAI), vol. 3955, pp. 520–524. Springer, Heidelberg (2006)
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Kanterakis, A., Kafetzopoulos, D., Moustakis, V., Potamias, G. (2008). Mining Gene Expression Profiles and Gene Regulatory Networks: Identification of Phenotype-Specific Molecular Mechanisms. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_10
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DOI: https://doi.org/10.1007/978-3-540-87881-0_10
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