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Graphical Modelling in Genetics and Systems Biology

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9521)

Abstract

Graphical modelling in its modern form was pioneered by Lauritzen and Wermuth [43] and Pearl [56] in the 1980s, and has since found applications in fields as diverse as bioinformatics [28], customer satisfaction surveys [37] and weather forecasts [1]. Genetics and systems biology are unique among these fields in the dimension of the data sets they study, which often contain several thousand variables and only a few tens or hundreds of observations. This raises problems in both computational complexity and the statistical significance of the resulting networks, collectively known as the “curse of dimensionality”. Furthermore, the data themselves are difficult to model correctly due to the limited understanding of the underlying phenomena. In the following, we will illustrate how such challenges affect practical graphical modelling and some possible solutions.

Keywords

  • Feature Selection
  • Gene Expression Data
  • Bayesian Network
  • Markov Network
  • Chain Graph

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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Scutari, M. (2015). Graphical Modelling in Genetics and Systems Biology. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_9

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