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Inference on Missing Values in Genetic Networks Using High-Throughput Data

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4973)

Abstract

High-throughput techniques investigating for example protein-protein or protein-ligand interactions produce vast quantity of data, which can conveniently be represented in form of matrices and can as a whole be regarded as knowledge networks. Such large networks can inherently contain more information on the system under study than is explicit from the data itself. Two different algorithms have previously been developed for economical and social problems to extract such hidden information. Based on three different examples from the field of proteomics and genetic networks, we demonstrate the great potential of applying these algorithms to a variety of biological problems.

Keywords

  • Positive Eigenvalue
  • Genetic Network
  • Knowledge Network
  • Physical Review Letter
  • Cytokine 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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© 2008 Springer-Verlag Berlin Heidelberg

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Koukolíková-Nicola, Z., Liò, P., Bagnoli, F. (2008). Inference on Missing Values in Genetic Networks Using High-Throughput Data. In: Marchiori, E., Moore, J.H. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2008. Lecture Notes in Computer Science, vol 4973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78757-0_10

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  • DOI: https://doi.org/10.1007/978-3-540-78757-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78756-3

  • Online ISBN: 978-3-540-78757-0

  • eBook Packages: Computer ScienceComputer Science (R0)