Data and Model Driven Hybrid Approach to Activity Scoring of Cyclic Pathways

  • Zerrin Işik
  • Volkan Atalay
  • Cevdet Aykanat
  • Rengül Çetin-Atalay
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 62)

Abstract

Analysis of large scale -omics data based on a single tool re- mains ine_cient to reveal molecular basis of cellular events. Therefore, data integration from multiple heterogeneous sources is highly desirable and required. In this study, we developed a data- and model-driven hy- brid approach to evaluate biological activity of cellular processes. Bio- logical pathway models were taken as graphs and gene scores were trans- ferred through neighbouring nodes of these graphs. An activity score describes the behaviour of a speci_c biological process was computed by owing of converged gene scores until reaching a target process. Biolog- ical pathway model based approach that we describe in this study is a novel approach in which converged scores are calculated for the cellular processes of a cyclic pathway. The convergence of the activity scores for cyclic graphs were demonstrated on the KEGG pathways.

Keywords

Activity Score KEGG Pathway Target Process Gene Score Rank Product 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dahlquist, K.D., Salomonis,N., Vranizan,K., Lawlor, S.C. and Conklin, B.R. (2002) GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet., 31, 19–20CrossRefGoogle Scholar
  2. 2.
    Mlecnik, B., Scheideler, M., Hackl, H., Hartler, J., Sanchez-Cabo, F. and Tra- janoski, Z. (2005) Pathway Explorer: web service for visualizing high-throughput expression data on biological pathways. Nucleic Acids Res., 33, W633-W637.CrossRefGoogle Scholar
  3. 3.
    Goffard N. and Weiller G. (2007) PathExpress: a web-based tool to identify relevant pathways in gene expression data. Nucleic Acids Res., 35, W176-W181.CrossRefGoogle Scholar
  4. 4.
    Isik Z., Atalay V., and Cetin-Atalay R. (2010) Evaluation of Signaling Cascades Based on the Weights from Microarray and ChIP-seq Data. Journal of Machine Learning Research, Workshop and Conference Proceedings, 8, 44–54.Google Scholar
  5. 5.
    Kang J., Gemberling M., Nakamura M., Whitby F.G., Handa H., Fairbrother W.G., Tantin D. (2009) A general mechanism for transcription regulation by Oct1 and Oct4 in response to genotoxic and oxidative stress. Genes Dev., 23(2), 208–222.CrossRefGoogle Scholar
  6. 6.
    Murray J.I., Whitfield M.L., Trinklein N.D., Myers R.M., Brown P.O., Botstein D. (2004) Diverse and specific gene expression responses to stresses in cultured human cells. Mol Biol Cel, 15(5), 2361–2374.CrossRefGoogle Scholar
  7. 7.
    Breitling R., Armengaud P., Amtmann A., Herzyk P. (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Letters, 573, 83–92.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Zerrin Işik
    • 1
  • Volkan Atalay
    • 1
  • Cevdet Aykanat
    • 2
  • Rengül Çetin-Atalay
    • 3
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Department of Computer EngineeringBilkent UniversityAnkaraTurkey
  3. 3.Department of Molecular Biology and GeneticsBilkent UniversityAnkaraTurkey

Personalised recommendations