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Discovering Hidden Pathways in Bioinformatics

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 7548)

Abstract

The elucidation of biological networks regulating the metabolic basis of disease is critical for understanding disease progression and in identifying therapeutic targets. In molecular biology, this process often starts by clustering expression profiles which are candidates for disease phenotypes. However, each cluster may comprise several overlapping processes that are active in the cluster. This paper outlines empirical results using methods for blind source separation to map the pathways of biomarkers driving independent, hidden processes that underpin the clusters. The method is applied to a protein expression data set measured in tissue from breast cancer patients (n=1,076).

Keywords

  • clustering
  • independent components
  • hidden sources

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© 2012 Springer-Verlag Berlin Heidelberg

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Lisboa, P.J.G. et al. (2012). Discovering Hidden Pathways in Bioinformatics. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-35686-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35685-8

  • Online ISBN: 978-3-642-35686-5

  • eBook Packages: Computer ScienceComputer Science (R0)