Using Bayesian Networks to Analyze Gene Expression Data

Using Bayesian Networks to Analyze Gene Expression Data

  • Project Goals
  • A quick presentation
  • Interactive tour of results
  • On line papers
  • Who we are
  • Mail us
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    Project Goal

    Massive amounts of gene expression profiles are quickly accumulating. This is due to the development of biotechnologies such as array based hybridization. Our goal is to develop algorithms and computational tools that can extract meaningful information about gene regulation and function from this data. We use methods for learning Bayesian networks to recover the structure of regulatory interactions between the different genes.
    We present here some preliminary results.
     

    Presentations

  • Using Bayesian Networks to Analyze Gene Expression Data   - an overview of our project.
  • A tutorial on Bayesian Networks

  • An interactive tour of our results

    We present here the results of our learning methods on data from the Yeast cell cycle analysis project published by Spellman et al. (1998) in Molecular Biology of the Cell. We thank the lab at Stanford for making this data available, and the lab members for the courteous help they gave us.
    We applied our methods to a data set of 800 genes that were found to be regulated by the cell cycle. These genes were clustered into 8 clusters in Spellman et al. (see their figure ). We learned our genetic network using no prior knowledge or assumptions ("Tabula Rasa"). We present an interactive tour of the learned networks .
     
  • All 800 genes

  • Instructions:


    Papers

  • Using Bayesian Networks to Analyze Expression Data N. Friedman, I. Nachman and D. Pe'er (A technical report describing this work. Submitted to RECOMB 2000).
  • Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate algorithm". N. Friedman, I. Nachman and D. Pe'er (UAI 99)
  • Data Analysis with Bayesian Networks: A Bootstrap Approach. N. Friedman, M. Goldszmidt, and A. Wyner. (UAI 99)

  • Who are we

  • Nir Friedman

  • Iftach Nachman
  • Dana Pe`er


  • In collaboration with
  • Michal Linial Life Sciences Hebrew University
  • Moises Goldszmidt SRI international