Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method

Date

2015-11-12

Author

Ozsoy, Makbule Gulcin
Polat, Faruk
Alhajj, Reda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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. However, 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 aspects between recommendation systems and GRNs, we decided to map the GRNs inspiring problem into a recommendation problem and then used a known recommendation method to predict gene relationships based on multiple data sources, 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 reported results show that using information from multiple sources improves the performance. Also, we observed that employing an approach from the recommendation systems domain revealed interesting results and good performance.

Subject Keywords

Recommendation Systems, Collaborative Filtering, Pareto Dominance, Multiple Data Sources, Gene Regulatory Networks

URI

https://hdl.handle.net/11511/55447

Conference Name

IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. G. Ozsoy, F. Polat, and R. Alhajj, “Inference of Gene Regulatory Networks Via Multiple Data Sources and a Recommendation Method,” presented at the IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making, Washington, DC, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55447.