Jowhara Fahad Al-Shobaili
ABSTRACT
One of the most challenging knowledge extraction tasks in bioinformatics is the reverse engineering of gene regulatory networks (GRNs) from DNA microarray gene expression data. GRNs represent the interaction between genes within the cell of living organisms and regulate signals from the cell state and the outside environment resulting in activation or inhibition of relevant genes. The "big data" generated by high throughput technologies have to be computationally managed with great care since it has a direct and decisive impact on human health. To address one of the most motivating ecosystems issues at the cellular level, this paper proposes a new Bayesian method for GRN estimation. The algorithm constructs GRNs by uniting three-gene subnetworks and improves the time complexity with respect to similar methods by using a depth-first strategy search in the GRNs space.
- Hamdi-Cherif, A. 2010. Intelligent control and biological regulation for bioinformatics. Int. J. of Mathematical Models and Methods In Applied Sciences 4, 2, (June 2010), 93--104. DOI=http://www.naun.org/multimedia/NAUN/m3as/19-283.pdfGoogle Scholar
- Ram, R. and Chetty, M. 2011. Modelling gene regulatory networks using computational intelligence techniques, In Das, S., Caragea, D., Welsh, S., and Hsu, W.S. (Ed.) Handbook of Research on Computational Methodologies in Gene Regulatory Networks, (Jan 2011), pp. 244--265.Google Scholar
- Bauer, S. and Robinson, P. 2011. Bayesian networks for modeling and inferring gene regulatory networks, In Das, S., Caragea, D., Welsh, S., and Hsu, W.S. (Ed.) Handbook of Research on Computational Methodologies in Gene Regulatory Networks, (Jan 2011), pp. 57--78.Google Scholar
- Beal, M., Falciani, F., Ghahramani, Z., Rangel, C. and Wild, D.L. 2005. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, Bioinformatics, 21, 3, 349--356. Google ScholarDigital Library
- Ram, R. and Chetty, M. 2011. A Markov-blanket-based model for gene regulatory network inference, IEEE-TCBB, 8, 2, (April 2011), pp. 353--367. Google ScholarDigital Library
- Watanabe, Y., Seno, S. Takenaka, Y., and Matsuda, H. 2012. An estimation method for inference of gene regulatory network using Bayesian network with uniting of partial problems, BMC Genomics, 13(Suppl1):S12. doi:10.1186/1471-2164-13-S1-S12 http://www.biomedcentral.com/1471-2164/13/S1/S12Google ScholarCross Ref
- Crombach, A. and Hogeweg, P. 2008. Evolution of evolvability in gene regulatory networks, PLoS Comput. Biol. 4(7):e1000112.doi:10.1371/journal.pcbi.1000112.Google ScholarCross Ref
- Hamdi-Cherif, A. 2010. Integrating machine learning in intelligent bioinformatics. WSEAS Transactions on Computers. 9, 4, (Apr. 2010), 406--417. DOI=http://www.wseas.us/e-library/transactions/computers/2010/89-530.pdf Google ScholarDigital Library
- Simon, R. 2011. Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n). SIGKDD Explorations, 5, 2, (Nov. 2011), 31--36. Google ScholarDigital Library
- Babu, M.M. 2004. An introduction to microarray data analysis. In Grant, R.P., (Ed.) Computational Genomics: Theory and Application. Horizon Bioscience.Google Scholar
- de la Fuente, A. 2011. What are gene regulatory networks?, In Das, S., Caragea, D., Welsh, S., and Hsu, W.S. (Ed.) Handbook of Research on Computational Methodologies in Gene Regulatory Networks, (Jan 2011), pp. 1--27.Google Scholar
- Chen, B.-S., Yang, S.-K., Lan, C.-Y. and Chuang, Y.-J. 2008. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining, BMC Medical Genomics, 1, 46, http://www.biomedcentral.com/1755-8794/1/46Google Scholar
- Kitano, H., 2002. Systems biology: a brief overview, Science, 295:1662--1664.Google ScholarCross Ref
- Gonçalves, A.T.F., and Costa, E.J.F. 2011. A Model for a heterogeneous genetic network, In Das, S., Caragea, D., Welsh, S., and Hsu, W.S. (Ed.) Handbook of Research on Computational Methodologies in Gene Regulatory Networks, (Jan 2011), pp. 523--545.Google Scholar
- {Cil07} Ciliberti S., Martin, O.C., and Wagner, A. 2007. Innovation and robustness in complex regulatory gene networks, Proc Natl Acad Sci USA 104:13591--13596,Google ScholarCross Ref
- Ando, S. and Iba, H. 2000. Quantitative modeling of gene regulatory network: identifying the network by means of genetic algorithms, Genome Informatics, 11, 278--280.Google Scholar
- Russell, S.J., Norvig, P. 2003. Artificial Intelligence: A Modern Approach, Chapter 14: Probabilistic Reasoning, 2nd Ed., Prentice Hall. Google ScholarDigital Library
- Van den Bulcke, T., Van Leemput, K., Naudts, B., Van Remortel, P., Ma, H., Verschoren, A., De Moor, B. and Marchal, K. 2006. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics. 7, 43. doi: 10.1186/1471-2105-7-43Google ScholarCross Ref
- Meyer, P., Cokelaer, T., Chandran, D., Kim, K.H., Loh, P.-R. et al. Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach BMC Systems Biology, 8, 1, p. 13, 2014. doi:10.1186/1752-0509-8-13Google Scholar
- Costello, J.C. et al., A community effort to assess and improve drug sensitivity prediction algorithms Nature BioTechnology, June 2014. doi:10.1038/nbt.2877Google Scholar
Index Terms
- Gene regulatory networks estimation using uniting Bayesian subnetworks
Recommendations
Big data fuzzy management methods in gene regulatory networks inference: a review
MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystemsTo address one of the most challenging ecosystems issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence on the ...
Integration of epigenetic data in Bayesian network modeling of gene regulatory network
PRIB'11: Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformaticsThe reverse engineering of gene regulatory network (GRN) is an important problem in systems biology. While gene expression data provide a main source of insights, other types of data are needed to elucidate the structure and dynamics of gene regulation. ...
Inferring Gene Regulatory Networks From Expression Data by Discovering Fuzzy Dependency Relationships
For one to infer the structures of a gene regulatory network (GRN), it is important to identify, for each gene in the GRN, which other genes can affect its expression and how they can affect it. For this purpose, many algorithms have been developed to ...
Comments