A comprehensive comparison of association estimators for gene network inference algorithms

Motivation: Gene network inference (GNI) algorithms enable the researchers to explore the interactions among the genes and gene products by revealing these interactions. The principal process of the GNI algorithms is to obtain the association scores among genes. Although there are several association estimators used in different applications, there is no commonly accepted estimator as the best one for the GNI applications. In this study, 27 different interaction estimators were reviewed and 14 most promising ones among them were evaluated by using three popular GNI algorithms with two synthetic and two real biological datasets belonging to Escherichia coli bacteria and Saccharomyces cerevisiae yeast. Influences of the Copula Transform (CT) pre-processing operation on the performance of the interaction estimators are also observed. This study is expected to assist many researchers while studying with GNI applications. Results: B-spline, Pearson-based Gaussian and Spearman-based Gaussian association score estimators outperform the others for all datasets in terms of the performance and runtime. In addition to this, it is observed that, when the CT operation is used, inference performances of the estimators mostly increase, especially for two synthetic datasets. Detailed evaluations and discussions are given in the experimental results.