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Revealing Structure of Complex Biological Systems Using Bayesian Networks

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Network Science

Abstract

Bayesian networks represent statistical dependencies among variables; they are able to model multiple types of relationships, including stochastic, non-linear, and arbitrary combinatoric. Such flexibility has made them excellent models for reverse-engineering structure of complex networks. This chapter reviews the use of Bayesian networks for probing structure of biological systems. We begin with an introduction to Bayesian networks, addressing especially issues of their interpretation as relates to understanding system structure. We then cover how Bayesian network structures are learned from data, considering a popular scoring metric, the BDe, in detail. We finish by reviewing the uses of Bayesian networks in biological systems to date and the concurrent advances in Bayesian network methodology tailored for use in biology.

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Notes

  1. 1.

    It is worth noting that the addition of tired has formed a v-structure [60] that has no other equivalence class representation; this thus uniquely orients all links in the BN, enabling causal interpretation.

  2. 2.

    Note that with basic DBNs, which include self-links from a variable in one time to itself in the next, the only link reversals would be those to variables in t+Δt without influence from different variables in t: the convergence of self-links and links from other variables creates a combinatoric v-structure (as in Fig. 9.3(c)).

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I am grateful to Dr. Charles Twardy for a critical reading of the manuscript.

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    1. V. Anne Smith
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    1. , Dept. Mathematics, University of Strathclyde, Glasgow, G1 1XH, United Kingdom

      Ernesto Estrada

    2. , Dept. Computer & Information Sciences, University of Strathclyde, Richmond Street 26, Glasgow, G1 1XH, United Kingdom

      Maria Fox

    3. Dept. Mathematics, University of Strathclyde, Glasgow, G1 1XH, United Kingdom

      Desmond J. Higham

    4. , Dept. Physics & Applied Physics, University of Strathclyde, Rottenrow 107, Glasgow, G4 0NG, United Kingdom

      Gian-Luca Oppo

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    Smith, V.A. (2010). Revealing Structure of Complex Biological Systems Using Bayesian Networks. In: Estrada, E., Fox, M., Higham, D., Oppo, GL. (eds) Network Science. Springer, London. https://doi.org/10.1007/978-1-84996-396-1_9

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