Generalized Centroid Estimators in Bioinformatics

TL;DR

This paper introduces a versatile class of estimators tailored for high-dimensional binary problems in bioinformatics, aligning with common accuracy measures and enabling efficient computation and unified interpretation of existing algorithms.

Contribution

It presents a general, extendable framework for designing maximum expected accuracy-based estimators applicable to various bioinformatics problems.

Findings

Estimators align with measures like sensitivity, PPV, MCC, and F-score.

Efficient algorithms are developed for many cases.

Provides a unified interpretation of existing bioinformatics algorithms.

Abstract

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.