TY - JOUR

T1 - A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA)

AU - Hamada, Michiaki

AU - Asai, Kiyoshi

PY - 2012/5/1

Y1 - 2012/5/1

N2 - 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.

AB - 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.

KW - RNA

KW - algorithms

KW - alignment

KW - secondary structure

KW - sequence analysis

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U2 - 10.1089/cmb.2011.0197

DO - 10.1089/cmb.2011.0197

M3 - Review article

C2 - 22313125

AN - SCOPUS:84860704698

VL - 19

SP - 532

EP - 549

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 5

ER -