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Article

XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression

1
Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), 265 04 Patras, Greece
2
Department of Medicine, University of Patras, 265 04 Patras, Greece
Academic Editor: Michael Banf
Received: 11 March 2021 / Revised: 17 April 2021 / Accepted: 19 April 2021 / Published: 20 April 2021
(This article belongs to the Special Issue Inference of Gene Regulatory Networks Using Randomized Algorithms)
In Systems Biology, the complex relationships between different entities in the cells are modeled and analyzed using networks. Towards this aim, a rich variety of gene regulatory network (GRN) inference algorithms has been developed in recent years. However, most algorithms rely solely on gene expression data to reconstruct the network. Due to possible expression profile similarity, predictions can contain connections between biologically unrelated genes. Therefore, previously known biological information should also be considered by computational methods to obtain more consistent results, such as experimentally validated interactions between transcription factors and target genes. In this work, we propose XGBoost for gene regulatory networks (XGRN), a supervised algorithm, which combines gene expression data with previously known interactions for GRN inference. The key idea of our method is to train a regression model for each known interaction of the network and then utilize this model to predict new interactions. The regression is performed by XGBoost, a state-of-the-art algorithm using an ensemble of decision trees. In detail, XGRN learns a regression model based on gene expression of the two interactors and then provides predictions using as input the gene expression of other candidate interactors. Application on benchmark datasets and a real large single-cell RNA-Seq experiment resulted in high performance compared to other unsupervised and supervised methods, demonstrating the ability of XGRN to provide reliable predictions. View Full-Text
Keywords: gene regulatory networks; gene expression; XGBoost; regression gene regulatory networks; gene expression; XGBoost; regression
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MDPI and ACS Style

Dimitrakopoulos, G.N. XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression. Computation 2021, 9, 48. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9040048

AMA Style

Dimitrakopoulos GN. XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression. Computation. 2021; 9(4):48. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9040048

Chicago/Turabian Style

Dimitrakopoulos, Georgios N. 2021. "XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression" Computation 9, no. 4: 48. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9040048

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