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A classification-based framework for predicting and analyzing gene regulatory response.

Anshul Kundaje1, Manuel Middendorf, Mihir Shah

  • 1Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu

BMC Bioinformatics
|May 26, 2006
PubMed
Summary
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A new Robust GeneClass algorithm improves gene transcriptional regulation prediction in yeast. This enhanced model offers greater stability and efficiency, aiding in the discovery of condition-specific gene regulation programs and signaling pathways.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Introduced GeneClass, a supervised learning algorithm for predicting gene transcriptional regulation in model organisms like Saccharomyces cerevisiae.
  • GeneClass utilizes regulatory region motifs and transcription factor expression levels to predict gene up/down-regulation.
  • The algorithm formulates prediction as a classification problem using an alternating decision tree, a generalization of decision trees.

Purpose of the Study:

  • To introduce a robust, stable, and computationally efficient version of the GeneClass algorithm.
  • To enhance the scalability and reliability of predictive models for gene regulation.
  • To develop a post-processing framework for biological interpretation of regulatory programs and signaling pathways.

Main Methods:

Related Experiment Videos

  • Developed Robust GeneClass with a stabilized boosting variant to retain correlated features.
  • Implemented fast matrix computation for scalability to large datasets.
  • Incorporated genome-wide ChIP-chip data and an improved noise model for gene expression data.

Main Results:

  • Demonstrated improved scalability and stability of features in the learned prediction tree using Robust GeneClass.
  • Analyzed yeast environmental stress datasets, training and testing on all genes with a comprehensive set of regulators.
  • Identified novel hypotheses for transcriptional and post-transcriptional regulation of protein chaperones and Nrg1/Nrg2 targets in yeast.

Conclusions:

  • Robust GeneClass provides a more scalable, stable, and interpretable framework for gene transcriptional regulation studies.
  • The post-processing framework facilitates the discovery of condition-specific regulatory programs and potential signaling pathways.
  • The enhanced algorithm and source code are available for broader research application.