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Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks

  • Luowen Liu
  • Jing LiuEmail author
Methodologies and Application
  • 38 Downloads

Abstract

Reconstructing gene regulatory networks (GRNs) from gene expression data is an important and challenging problem in system biology. In general, the problem of reconstructing GRNs can be modeled as an optimization problem. Recurrent neural network (RNN) has been widely used for GRNs. However, in a real GRN, the number of genes is very large and the relationships between genes are usually very sparse. In this paper, we design a memetic algorithm to learn partial parameters of RNN, and develop a framework based on the least absolute shrinkage and selection operator (LASSO) to reconstruct GRNs based on RNN, which is termed as MALASSORNN-GRN. In the LASSO, the task of reconstructing GRNs is decomposed into a sparse signal reconstructing problem. In the experiments, MALASSORNN-GRN is applied on synthetic data and well-known benchmark datasets DREAM3 and DREAM4. The effect of parameters on MALASSORNN-GRN is discussed, and MALASSORNN-GRN is compared with three other algorithms which are all state-of-the-art RNN learning algorithms. The results show that MALASSORNN-GRN performs best and is capable of reconstructing large-scale GRNs.

Graphic abstract

Keywords

Gene regulatory networks Memetic algorithm LASSO Recurrent neural network 

Notes

Acknowledgements

This work was supported in part by the General Program of National Natural Science Foundation of China (NSFC) under Grant 61773300 and in part by the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China, under Grant 2017JZ017.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityXi’anChina

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