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Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data

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Bioinformatics Research and Applications (ISBRA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8492))

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Abstract

Dynamic aspects of regulatory networks are typically investigated by measuring relevant variables at multiple points in time. Current state-of-the-art approaches for gene network reconstruction directly build on such data, making the strong assumption that the system evolves in a synchronous fashion and in discrete time. However, omics data generated with increasing time-course granularity allow to model gene networks as systems whose state evolves in continuous time, thus improving the model’s expressiveness. In this work continuous time Bayesian networks are proposed as a new approach for regulatory network reconstruction from time-course expression data. Their performance is compared to that of two state-of-the-art methods: dynamic Bayesian networks and Granger causality. The comparison is accomplished using both simulated and experimental data. Continuous time Bayesian networks achieve the highest F-measure on both datasets. Furthermore, precision, recall and F-measure degrade in a smoother way than those of dynamic Bayesian networks and Granger causality, when the complexity of the gene regulatory network increases.

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Acerbi, E., Stella, F. (2014). Continuous Time Bayesian Networks for Gene Network Reconstruction: A Comparative Study on Time Course Data. In: Basu, M., Pan, Y., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2014. Lecture Notes in Computer Science(), vol 8492. Springer, Cham. https://doi.org/10.1007/978-3-319-08171-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-08171-7_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08170-0

  • Online ISBN: 978-3-319-08171-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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