41. Unsupervised Learning for Gene Regulation Network Inference from Expression Data: A Review

  1. Mourad Elloumi3 and
  2. Albert Y. Zomaya4
  1. Mohamed Elati1 and
  2. Céline Rouveirol2

Published Online: 23 DEC 2010

DOI: 10.1002/9780470892107.ch41

Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications

Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications

How to Cite

Elati, M. and Rouveirol, C. (2011) Unsupervised Learning for Gene Regulation Network Inference from Expression Data: A Review, in Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications (eds M. Elloumi and A. Y. Zomaya), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470892107.ch41

Editor Information

  1. 3

    Unit of Technologies of Information and Communication (UTIC) and University of Tunis-El Manar, Tunisia

  2. 4

    School of Information Technologies, The University of Sydney, Australia

Author Information

  1. 1

    Institute of Systems and Synthetic Biology, Evry University - Genopole, Evry, France

  2. 2

    LIPN, UMR CNRS, Institute Galil′ee, University Paris-Nord, France

Publication History

  1. Published Online: 23 DEC 2010
  2. Published Print: 18 JAN 2011

ISBN Information

Print ISBN: 9780470505199

Online ISBN: 9780470892107

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Keywords:

  • unsupervised learning for gene regulation network inference - from expression data;
  • gene expression, data and analysis - amount of mRNA produced during transcription, measure of how active or functional a gene is;
  • formidable challenge, gene regulatory network dissection - delineating how eukaryote cells coordinate and govern patterns of gene expression leading to a phenotype

Summary

This chapter contains sections titled:

  • Introduction

  • Gene Networks: Definition and Properties

  • Gene Expression: Data and Analysis

  • Network Inference as an Unsupervised Learning Problem

  • Correlation-Based Methods

  • Probabilistic Graphical Models

  • Constraint-Based Data Mining

  • Validation

  • Conclusion and Perspectives

  • References