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

Michiaki Hamada, Kiyoshi Asai

Research output: Contribution to journalReview article

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)532-549
Number of pages18
JournalJournal of Computational Biology
Volume19
Issue number5
DOIs
Publication statusPublished - 2012 May 1

Keywords

  • RNA
  • algorithms
  • alignment
  • secondary structure
  • sequence analysis

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA)'. Together they form a unique fingerprint.

  • Cite this