Impacts of the different spline orders on the B-spline association estimator

Zeyneb Kurt, Nizamettin Aydin, Gökmen Altay*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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. B-spline 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.

Original languageEnglish
Title of host publication13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013 - Chania, Greece
Duration: 10 Nov 201313 Nov 2013

Publication series

Name13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013

Conference

Conference13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013
CountryGreece
CityChania
Period10/11/1313/11/13

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