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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4973)


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.


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Fachhochschule NordwestschweizHochschule für TechnikWindischSwitzerland
  2. 2.The Computer LaboratoryUniversity of CambridgeCambridgeUK
  3. 3.Department of Energy, University of Florence, Via S. Marta, 3 50139 Firenze. Also CSDC and INFN, sez. Firenze 

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