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Multiobjective optimization in bioinformatics and computational biology.

Julia Handl1, Douglas B Kell, Joshua Knowles

  • 1School of Chemistry, The University of Manchester, Manchester, UK.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 3, 2007
PubMed
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This review explores multiobjective optimization (MOO) in bioinformatics and computational biology. It identifies five contexts driving MOO applications and suggests future research directions for these complex biological problems.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Multiobjective Optimization

Background:

  • Multiobjective optimization (MOO) is increasingly relevant in complex biological data analysis.
  • Understanding the drivers for MOO application is crucial for advancing the field.

Purpose of the Study:

  • To review the current applications of MOO in bioinformatics and computational biology.
  • To identify and categorize the contexts that necessitate MOO in these scientific domains.
  • To propose future research avenues for MOO in biological sciences.

Main Methods:

  • A comprehensive survey of existing literature on MOO in bioinformatics and computational biology.
  • Categorization of MOO applications based on identified contexts.
  • Analysis of the reasons for MOO adoption within each application area.

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Main Results:

  • The review consolidates current MOO applications across various bioinformatics and computational biology subfields.
  • Five distinct contexts driving the use of MOO in biological research were identified.
  • The identified contexts provide a framework for understanding past MOO usage and predicting future trends.

Conclusions:

  • MOO is a valuable technique for addressing multifaceted challenges in bioinformatics and computational biology.
  • The identified contexts offer a novel perspective for the application and development of MOO methods in life sciences.
  • Further research leveraging these contexts can enhance the impact of MOO on biological discovery.