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Network Inference in Molecular BiologyStep 3: Using Time-Series Data

Network Inference in Molecular Biology: Step 3: Using Time-Series Data [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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Network Inference in Molecular BiologyStep 3: Using Time-Series Data

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Publisher
Springer New York
Copyright
© The Author(s) 2012
ISBN
978-1-4614-3112-1
Pages
51 –76
DOI
10.1007/978-1-4614-3113-8_4
Publisher site
See Chapter on Publisher Site

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.]

Published: May 24, 2012

Keywords: Mutual Information; Gene Network; True Positive Rate; Cumulative Density Function; Dynamic Bayesian Network

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