Abstract
Time-series data gives information about the values of genes at a series of consecutive time points. This temporal information can be exploited to infer directionality of edges, or help to infer causal relations between genes. However, adding temporal information also creates a more complex dataset. It adds interdependencies between experiments (time-points) that don’t exist in steady-state data, so more care has to be taken in analysis. Three types of algorithms will be presented in this section: mutual information, ordinary differential equations with l1 regularization, and dynamic Bayesian Networks. Each of these approaches makes different assumptions about the data.
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Lingeman, J.M., Shasha, D. (2012). Step 3: Using Time-Series Data. In: Network Inference in Molecular Biology. SpringerBriefs in Electrical and Computer Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3113-8_4
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DOI: https://doi.org/10.1007/978-1-4614-3113-8_4
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Publisher Name: Springer, New York, NY
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Online ISBN: 978-1-4614-3113-8
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