Causal Reasoning on Biological Networks: Interpreting Transcriptional Changes

(Extended Abstract)
  • Leonid Chindelevitch
  • Daniel Ziemek
  • Ahmed Enayetallah
  • Ranjit Randhawa
  • Ben Sidders
  • Christoph Brockel
  • Enoch Huang
Conference paper

DOI: 10.1007/978-3-642-20036-6_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6577)
Cite this paper as:
Chindelevitch L. et al. (2011) Causal Reasoning on Biological Networks: Interpreting Transcriptional Changes. In: Bafna V., Sahinalp S.C. (eds) Research in Computational Molecular Biology. RECOMB 2011. Lecture Notes in Computer Science, vol 6577. Springer, Berlin, Heidelberg

Abstract

Over the past decade gene expression data sets have been generated at an increasing pace. In addition to ever increasing data generation, the biomedical literature is growing exponentially. The PubMed database (Sayers et al., 2010) comprises more than 20 million citations as of October 2010. The goal of our method is the prediction of putative upstream regulators of observed expression changes based on a set of over 400,000 causal relationships. The resulting putative regulators constitute directly testable hypotheses for follow-up.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Leonid Chindelevitch
    • 1
  • Daniel Ziemek
    • 1
  • Ahmed Enayetallah
    • 2
  • Ranjit Randhawa
    • 1
  • Ben Sidders
    • 3
  • Christoph Brockel
    • 4
  • Enoch Huang
    • 1
  1. 1.Computational Sciences Center of EmphasisPfizer Worldwide Research and DevelopmentCambridgeUSA
  2. 2.Compound Safety PredictionPfizer Worldwide Medicinal ChemistryGrotonUSA
  3. 3.eBiology, Pfizer Worldwide Research and Development, SandwichKentUK
  4. 4.Translational and BioinformaticsPfizer Business TechnologiesCambridgeUSA

Personalised recommendations