Gene regulatory networks (GRNs) are composed of biological components, including genes, proteins and metabolites, and their interactions. In general, computational methods are used to infer the connections among these components. The computational methods should take into account the general features of the GRNs, which are sparseness, scale-free topology, modularity and structure of the inferred networks. In this work, observing the common features of recommendation systems and GRNs, we used a known recommendation method to predict the gene relationships, e.g., which molecules regulate others. The method we used is based on Pareto dominance and collaborative filtering. For the experiments, we used a combination of two datasets, namely microarray data and transcription factor (TF) binding data. The results show that using information from multiple sources improves the performance. Also, we observed that employing an approach from the recommendation systems domain revealed good performance.

Cite the following paper whenever all or any part of this code is used.

• Makbule Gulcin Ozsoy, Faruk Polat, Reda Alhajj: Inference of gene regulatory networks via multiple data sources and a recommendation method. BIBM 2015: 661-664