A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA)

Michiaki Hamada, Kiyoshi Asai

研究成果: Review article査読

14 被引用数 (Scopus)

抄録

Many estimation problems in bioinformatics are formulated as point estimation problems in a high-dimensional discrete space. In general, it is difficult to design reliable estimators for this type of problem, because the number of possible solutions is immense, which leads to an extremely low probability for every solution-even for the one with the highest probability. Therefore, maximum score and maximum likelihood estimators do not work well in this situation although they are widely employed in a number of applications. Maximizing expected accuracy (MEA) estimation, in which accuracy measures of the target problem and the entire distribution of solutions are considered, is a more successful approach. In this review, we provide an extensive discussion of algorithms and software based on MEA. We describe how a number of algorithms used in previous studies can be classified from the viewpoint of MEA. We believe that this review will be useful not only for users wishing to utilize software to solve the estimation problems appearing in this article, but also for developers wishing to design algorithms on the basis of MEA.

本文言語English
ページ(範囲)532-549
ページ数18
ジャーナルJournal of Computational Biology
19
5
DOI
出版ステータスPublished - 2012 5 1
外部発表はい

ASJC Scopus subject areas

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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