Impacts of the Different Spline Orders on the B-spline Association Estimator

Abstract

Gene Network Inference (GNI) algorithms enable searching the interactions among the several cell molecules. Many application fields such as computational biology and pharmacology utilize the GNI algorithms to illustrate the interaction networks of the cell molecules. Association score estimation is the most crucial step of the GNI applications. Bspline is a popular approach, which efficiently estimates the interaction scores between the variable (gene) pairs. In this study inference performance of the B-spline estimator according to the selected spline order is examined. In addition to evaluating B-spline performance according to the spline order, influences of using a frequently used pre-processing operation Copula Transform on the performance of B-spline is also examined. Conservative Causal Core network (C3NET) GNI algorithm is used in the experiments. At the overall analysis, B-spline estimator with the spline order 2 gave the best inference performance among the selected spline orders from 1 to 10.