A Classification of Bioinformatics Algorithms from the Viewpoint of Maximizing Expected Accuracy (MEA)
A Classification of Bioinformatics Algorithms from the Viewpoint of Maximizing Expected Accuracy (MEA)
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DOI：
10.1089/cmb.2011.0197
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发表时间：
2012-05-01
影响因子：
1.7
通讯作者：
Asai, Kiyoshi
中科院分区：
文献类型：
--
作者：
Hamada, Michiaki;Asai, Kiyoshi
生物信息学中的许多估计问题都被描述为高维离散空间中的点估计问题。一般来说，很难为这类问题设计可靠的估计器，因为可能的解的数量是巨大的，这导致每一个解的概率都非常低--即使是概率最高的那个。因此，最大得分和最大似然估计器在这种情况下不能很好地工作，尽管它们在许多应用中被广泛使用。最大化期望精度(MEA)估计是一种更成功的方法，其中考虑了目标问题的精度度量和解的整个分布。在这篇综述中，我们对基于MEA的算法和软件进行了广泛的讨论。我们描述了以前研究中使用的一些算法如何从MEA的角度进行分类。我们相信，这篇综述不仅对希望利用软件解决本文中出现的估计问题的用户有用，而且对希望在MEA基础上设计算法的开发人员也有用。
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.