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PFastNCA: Parallel Fast Network Component Analysis for Gene Regulatory Network

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The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

One of the gene expression data analysis tasks is the Gene regulatory network analysis. Gene regulatory network is concerned in the topological organization of genes interactions. Moreover, the regulatory network is important for understanding the normal cell physiology and pathological phenotypes. However, the main challenge facing gene regulatory network algorithms is the data size. Where, the algorithm runtime is proportional to the data size. This paper presents a parallel algorithm for gene regulatory network (PFastNCA) which is an improved version of FastNCA. PFastNCA enhanced the main core of FastNCA which is the connectivity matrix estimation using a distributed computing model. Where, the work is divided among N processing nodes, PFastNCA is more efficient than FastNCA. It also achieved a better performance and speedup reached 1.91.

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References

  1. Nair, A.: Computational biology & bioinformatics - a gentle overview. Commun. Comput. Soc. India 30(1), 7–12 (2007)

    Google Scholar 

  2. Cosmides, L., Tooby, J.: From Function to Structure: The Role of Evolutionary Biology and Computational Theories in Cognitive Neuroscience. The MIT Press, Cambridge (1995)

    Google Scholar 

  3. Velculescu, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W.: Serial analysis of gene expression. Science 270(5235), 484–487 (1995)

    Article  Google Scholar 

  4. Durbin, R.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)

    Book  MATH  Google Scholar 

  5. Kelley, L.A., MacCallum, R.M., Sternberg, M.J.: Enhanced genome annotation using structural profiles in the program 3D-PSSM. J. Mol. Biol. 299(2), 501–522 (2000)

    Article  Google Scholar 

  6. Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R.W., Belle, A., Dephoure, N., O’Shea, E.K., Weissman, J.S.: Global analysis of protein expression in yeast. Nature 425(6959), 737–741 (2003)

    Article  Google Scholar 

  7. Janssen, P.J., Jones, W.A., Jones, D.T., Woods, D.R.: Molecular analysis and regulation of the glnA gene of the gram-positive anaerobe Clostridium acetobutylicum. J. Bacteriol. 170(1), 400–408 (1988)

    Article  Google Scholar 

  8. Dominguez, C., Boelens, R., Bonvin, A.M.: HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125(7), 1731–1737 (2003)

    Article  Google Scholar 

  9. Shortle, D.: Prediction of protein structure. Curr. Biol. 10(2), 49–51 (2000)

    Article  Google Scholar 

  10. Berrozpe, G., Schaeffer, J., Peinado, M.A., Real, F.X., Perucho, M.: Comparative analysis of mutations in the p53 and K-ras genes in pancreatic cancer. Int. J. Cancer 58(2), 185–191 (1994)

    Article  Google Scholar 

  11. Haefner, J.W.: Modeling Biological Systems: Principles and Applications. Springer, US (2005)

    MATH  Google Scholar 

  12. Rubin, G.M., Yandell, M.D., Wortman, J.R., Gabor, G.L., Nelson, C.R., Hariharan, I.K., Fortini, M.E., Li, P.W., Apweiler, R., Fleischmann, W.: Comparative genomics of the eukaryotes. Science 287(5461), 2204–2215 (2000)

    Article  Google Scholar 

  13. Dowsey, A.W.: High-Throughput Image Analysis for Proteomics. Citeseer (2005)

    Google Scholar 

  14. Churchill, G.A.: Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32(1), 490–495 (2002)

    Article  Google Scholar 

  15. Culf, A., Cuperlovic-Culf, M., Ouellette, R.: Carbohydrate microarrays: survey of fabrication techniques. OMICS J. Integr. Biol. 10(3), 289–310 (2006)

    Article  Google Scholar 

  16. Gasch, A., Spellman, P., Kao, C., Carmel-Harel, O., Eisen, M., Storz, G., Botstein, D., Brown, P.: Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11(12), 4241–4257 (2000)

    Article  Google Scholar 

  17. Schena, M., Shalon, D., Davis, R., Brown, P.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995). Washington

    Article  Google Scholar 

  18. Yang, Y., Choi, J., Choi, K., Pierce, M., Gannon, D., Kim, S.: BioVLAB-Microarray: microarray data analysis in virtual environment. In: IEEE Fourth International Conference on eScience (2008)

    Google Scholar 

  19. Haman, J., Valenta, Z.: Shrinkage approach for gene expression data analysis. EJBI 9(3), 2–8 (2013)

    Google Scholar 

  20. Aluru, S.: Handbook of Computational Molecular Biology. CRC Press, Boca Raton (2006)

    MATH  Google Scholar 

  21. Raychaudhuri, S., Stuart, J.M., Altman, R.B.: Principal components analysis to summarize microarray experiments: application to sporulation time series. In: Pacific Symposium on Biocomputing, pp. 455–466. NIH Public Access (2000)

    Google Scholar 

  22. Watkins, D.S.: Fundamentals of Matrix Computations, vol. 64, pp. 309–409. John Wiley & Sons, Chichester (2004)

    Google Scholar 

  23. Holter, N.S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J.R., Fedoroff, N.V.: Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc. Natl. Acad. Sci. 97(15), 8409–8414 (2000)

    Article  Google Scholar 

  24. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)

    Book  Google Scholar 

  25. Aapo, H.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  26. Liebermeister, W.: Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1), 51–60 (2002)

    Article  Google Scholar 

  27. Liao, J.C., Boscolo, R., Yang, Y.-L., Tran, L.M., Sabatti, C., Roychowdhury, V.P.: Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl. Acad. Sci. 100, 15522–15527 (2003)

    Article  Google Scholar 

  28. Chang, C., Ding, Z., Hung, Y.S., Fung, P.C.W.: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 24(11), 1349–1358 (2008)

    Article  Google Scholar 

  29. Jostins, L., Jaeger, J.: Reverse engineering a gene network using an asynchronous parallel evolution strategy. BMC Syst. Biol. 4(1), 17–33 (2010)

    Article  Google Scholar 

  30. Gregoretti, F., Belcastro, V., Di Bernardo, D., Oliva, G.: A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS ONE 5(4), e10179–e10183 (2010)

    Article  Google Scholar 

  31. Sales, G., Romualdi, C.: parmigene—a parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics 27(13), 1876–1877 (2011)

    Article  Google Scholar 

  32. Shi, H., Schmidt, B., Liu, W., Muller-Wittig, W.: Parallel mutual information estimation for inferring gene regulatory networks on GPUs. BMC Res. Notes 4(1), 189–194 (2011)

    Article  Google Scholar 

  33. Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95(25), 14863–14868 (1998)

    Article  Google Scholar 

  34. Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists. Macmillan, New York (1993)

    MATH  Google Scholar 

  35. Elsayad, D., Ali, A., Shedeed, H.A., Tolba, M.F.: PAGeneRN: parallel architecture for gene regulatory network. In: Handbook of Research on Machine Learning Innovations and Trends, pp. 762–786. IGI Global (2017)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Dina Elsayad, A. Ali, Howida A. Shedeed & M. F. Tolba

Authors
  1. Dina Elsayad
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  2. A. Ali
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  3. Howida A. Shedeed
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  4. M. F. Tolba
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Corresponding author

Correspondence to Dina Elsayad .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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Elsayad, D., Ali, A., Shedeed, H.A., Tolba, M.F. (2018). PFastNCA: Parallel Fast Network Component Analysis for Gene Regulatory Network. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_57

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

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