Authors: Makbule Gulcin Ozsoy; Faruk Polat; Reda Alhajj
Addresses: Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey ' Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey ' Department of Computer Science, University of Calgary, 2500 University Dr. NW Calgary, T2N 1N4 Alberta, Canada; Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey
Abstract: In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering approach enhanced with multiple criteria to predict the relationships of genes, i.e., which genes regulate others. We conduct experiments on two data sets to demonstrate the applicability and sustainability of our proposal. The first data set is composed of microarray data and Transcription Factor (TF) binding data, and it is evaluated by precision, recall and the F1-measure. The second data set is the Dream4 In Silico Network Challenge data set, and it is evaluated by the measures that are used during the challenge, namely the Area Under Precision and Recall curve (AUC-PR), the Area Under the Receiver Operating Characteristic curve (AUC-ROC) and their averages. The experimental results show that applying algorithms from the recommendation systems domain on the problem of inference of GRN structures is effective. Also, we observed that combining information from multiple data sets gives better results.
Keywords: GRNs; gene regulatory networks; recommendation systems; collaborative filtering; multiple data sources; Pareto dominance.
International Journal of Data Mining and Bioinformatics, 2019 Vol.22 No.2, pp.91 - 112
Available online: 18 May 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article