Comprehensive review of association estimators for the inference of gene networks

Abstract

Gene network inference (GNI) algorithms allow us to explore the vast amount of interactions among the molecules in cells. In almost all GNI algorithms the main process is to estimate association scores among the variables of the dataset. However, there is no commonly accepted estimator to compute association scores for the current GNI algorithms. In this paper the association estimators that might be used in GNI applications are reviewed. The aim is to prepare a comprehensive and comparative review of all the important association estimators available in the literature. We performed this main aim by presenting, classifying, comparing, and discussing them to reveal which association estimator is more suitable for use in GNI applications by considering only the information available in the literature. Twenty-seven different estimators from various areas are investigated. The estimators were compared according to the GNI performances in the literature. The most promising association estimators for the GNI applications are suggested. As a result of the study, we identified eight promising methods for effective use in GNI. We expect this study to assist many researchers before using those estimators in their own GNI studies.