  In a number of estimation problems in bioinformatics, accuracy measures of
the target problem are usually given, and it is important to design estimators
that are suitable to those accuracy measures. However, there is often a
discrepancy between an employed estimator and a given accuracy measure of the
problem. In this study, we introduce a general class of efficient estimators
for estimation problems on high-dimensional binary spaces, which representmany
fundamental problems in bioinformatics. Theoretical analysis reveals that the
proposed estimators generally fit with commonly-used accuracy measures (e.g.
sensitivity, PPV, MCC and F-score) as well as it can be computed efficiently in
many cases, and cover a wide range of problems in bioinformatics from the
viewpoint of the principle of maximum expected accuracy (MEA). It is also shown
that some important algorithms in bioinformatics can be interpreted in a
unified manner. Not only the concept presented in this paper gives a useful
framework to design MEA-based estimators but also it is highly extendable and
sheds new light on many problems in bioinformatics.
