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Step 3: Using Time-Series Data

  • Jesse M Lingeman
  • Dennis Shasha
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

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.

Keywords

Mutual Information Gene Network True Positive Rate Cumulative Density Function Dynamic Bayesian Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.New York UniversityNew YorkUSA

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