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Pattern recognition techniques in microarray data analysis: a survey.

Faramarz Valafar1

  • 1Department of Computer Science, San Diego State University, California 92182, USA. faramarz@sciences.sdsu.edu

Annals of the New York Academy of Sciences
|February 21, 2003
PubMed
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New pattern recognition techniques are essential for analyzing massive genetic data from technologies like microarray technology. This survey reviews data-mining methods used in bioinformatics to extract biological knowledge from these large datasets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Advancements in microarray technology generate vast amounts of genetic data.
  • Existing pattern recognition techniques are insufficient for mining this large-scale biological information.
  • There is a growing need for comprehensive surveys of data-mining techniques in bioinformatics.

Purpose of the Study:

  • To provide a foundational understanding of essential biology for non-biologists entering the field.
  • To survey and summarize data-mining techniques applied to microarray data analysis.
  • To offer examples of how these techniques extract biological knowledge, including sequence information.

Main Methods:

  • Review of existing literature on data-mining techniques for genetic data.

Related Experiment Videos

  • Explanation of fundamental biological concepts relevant to microarray data analysis.
  • Categorization and presentation of various data-mining approaches.

    • Main Results:

    • A curated overview of data-mining methodologies applicable to high-throughput genetic data.
    • Illustrative examples of technique applications in biological knowledge discovery.
    • Identification of key areas and challenges in mining microarray data.

    Conclusions:

    • This survey serves as an introductory guide for technical experts venturing into bioinformatics.
    • It highlights the critical role of data-mining in extracting meaningful insights from complex genetic datasets.
    • The field is rapidly evolving, necessitating continuous review of emerging techniques.