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[Constructing gene regulatory network from microarray data].

Wan-Lin Liu1, Dong Li, Yun-Ping Zhu

  • 1State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100850, China. liuwanlin@hupo.org.cn

Yi Chuan = Hereditas
|December 11, 2007
PubMed
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This review explores algorithms for constructing gene regulatory networks from microarray data. It highlights theoretical and methodological advancements, software, and future trends in bioinformatics research.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Context:

  • Microarray data is rapidly accumulating, driving demand for advanced bioinformatics tools.
  • Gene regulatory network (GRN) construction is a key focus for understanding cellular processes.
  • High-throughput microarray data offers a rich resource for inferring gene interactions.

Purpose:

  • To review the evolution of algorithms for gene regulatory network inference from microarray data.
  • To emphasize theoretical and methodological improvements in GRN construction algorithms.
  • To provide an overview of current software, platforms, and future research directions.

Summary:

  • This paper surveys algorithms used to build gene regulatory networks from microarray datasets.

Related Experiment Videos

  • It details advancements in the theoretical underpinnings and methodologies of these algorithms.
  • The review also covers available computational tools and discusses emerging trends.

    • Impact:

    • Facilitates a deeper understanding of complex regulatory relationships within biological systems.
    • Enables systematic analysis of cellular mechanisms and biological processes.
    • Supports the development of novel bioinformatics tools and strategies for genetic research.