An approach towards the reconstruction of regulatory networks

Rafael Teodósio Pereira, Hugo Costa, Rui Mendes

Resumo


Currently, one of the main issues addressed in the bioinformatics field is understanding the structure and behaviour of complex molecular interaction networks. Since most of the information available belongs to biomedical literature, a large part of this task entails selecting the relevant articles from a large body of papers. However, due to the rapidly increasing number of scientific papers, it is quite difficult to read all  the  papers that have been published about this subject. In order to accomplish this, this work is focused on developing methods for retrieving information from biological databases, gathering as much information as possible; to create an integrated repository, that is able to store and load this data and also to design a pipeline to allow the reconstruction of regulatory networks through using Biomedical Text Mining techniques.

Palavras-chave


Bioinformatics; Computer Science; Transcriptional Regulatory Networks; Data Integration

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Referências


Babu, M. M., Luscombe, N. M., Aravind, L., Gerstein, M., and Teichmann, S. A. (2004). Structure and evolution of transcriptional regulatory networks. Current opinion in structural biology, 14(3):283–291.

Barrett, C. L. and Palsson, B. O. (2006). Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLoS computational biology, 2(5):e52.

Ben-Tabou de Leon, S. and Davidson, E. H. (2007). Gene regulation: gene control network in development. Annual review of biophysics and biomolecular structure, 36:191.

Carrera, J., Rodrigo, G., Jaramillo, A., and Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome biology, 10(9):R96.

Crysmann, B., Frank, A., Kiefer, B., Krieger, H.-U., Muller, S., Neumann, G., Piskorski, J., Schafer, U., Siegel, M., Uszkoreit, H., and Xu, F. (2002). An integrated architecture for shallow and deep processing. In University of Pennsylvania, pages 441–448.

Cunningham, H. (2002). GATE, a general architecture for text engineering. Computers and the Humanities, 36(2):223–254.

Fielding, R. and Taylor, R. (2000). Principled design of the modernWeb architecture. Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium, 2(2):115–150.

Friedman, C., Kra, P., Yu, H., and Rzhetsky, A. (2001). GENIES : a natural-language processing system journal articles. Bioinformatics, 17.

Friedman, N., Linial, M., Nachman, I., and Pe’er, D. (2000). Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology, 7(3-4):601–620.

Gerner, M., Nenadic, G., and Bergman, C. M. (2010). LINNAEUS: a species name identification system for biomedical literature. BMC bioinformatics, 11:85.

Kubler, S., McDonald, R., Nivre, J., and Hirst, G. (2009). Dependency Parsing. Morgan and Claypool Publishers.

Neumann, G. and Piskorski, J. (2002). A shallow text processing core engine. Computational Intelligence, 18:451–476.

Pereira, R. and Mendes, R. (2014). Integrating Biological Databases in the Context of Transcriptional Regulatory Networks. International Journal of Bioscience, Biochemistry

and Bioinformatics, 4:345–350.

Sayers, E. (2008). Entrez Programming Utilities Help. National Center for Biotechnology Information (US).

Schlitt, T. and Brazma, A. (2007). Current approaches to gene regulatory network modelling. BMC bioinformatics, 8 Suppl 6:S9.

Shi, X. (2006). Sharing service semantics using SOAP-based and RESTWeb services. IT Professional, 8(2):18–24.

Spyns, P. (1996). Natural language processing in medicine: An overview. Methods of Information in Medicine, 35(4-5):285–301.

Temkin, J. M. and Gilder, M. R. (2003). Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics, 19(16):2046–2053.

van Someren, E. P., Wessels, L. F., and Reinders, M. J. (2000). Linear modeling of genetic networks from experimental data. Proceedings of International Conference on Intelligent Systems for Molecular Biology, 8:355–366.

Weaver, D. C. (1999). Modeling regulatory networks with weight matrices. In Pacific Symposium on Biocomputing, volume 4, pages 112–123.




DOI: http://dx.doi.org/10.5902/2448190422639

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