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Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference

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Abstract

Background

Simultaneous measurement of gene expression level for thousands of genes contains the rich information about many different aspects of biological mechanisms. A major computational challenge is to find methods to extract new biological insights from this wealth of data. Complex biological processes are often regulated under the various conditions or circumstances and associated gene interactions are dynamically changed depending on different biological contexts. Thus, inference of such dynamic relationships between genes with consideration of biological conditions is very challenging.

Method

In this study, we propose a comprehensive and integrated approach to infer the dynamic relationships between genes and evaluate this approach on three distinct gene networks.

Results

This study demonstrates the advantage of integrating Markov chain Monte Carlo (MCMC) simulation into a Bayesian mixture model to overcome the high-dimension, low sample size (HDLSS) problem as well as to identify context-specific biological modules. Such biological modules were identified through the summarization of sampled network structures obtained from MCMC simulation.

Conclusion

This novel approach gives a comprehensive understanding of the dynamically regulated biological modules.

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Acknowledgements

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education Grant 2017R1D1A1B03032457 and Hankuk University of Foreign Studies Research Fund (to Y.K.), and the Ministry of Science and ICT of Korea Grant 2014M3C9A3063544 and the Ministry of Education of Korea Grant 2016R1D1A1B03930209 (to J.K.).

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Authors and Affiliations

  1. Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, 17035, South Korea

    Younhee Ko

  2. Department of Animal Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Sandra L. Rodriguez-Zas

  3. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Sandra L. Rodriguez-Zas

  4. Department of Biomedical Science and Engineering, Konkuk University, Seoul, 05029, South Korea

    Jaebum Kim

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  1. Younhee Ko
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  2. Jaebum Kim
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  3. Sandra L. Rodriguez-Zas
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Correspondence to Jaebum Kim or Sandra L. Rodriguez-Zas.

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Younhee Ko, Jaebum Kim, and Sandra L. Rodriguez-Zas declare that they have no conflict of interest.

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This article does not contain any studies with human subjects or animals performed by any of the authors.

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Ko, Y., Kim, J. & Rodriguez-Zas, S.L. Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference. Genes Genom 41, 547–555 (2019). https://doi.org/10.1007/s13258-019-00789-8

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