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Hierarchical Generative Biclustering for MicroRNA Expression Analysis

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Research in Computational Molecular Biology (RECOMB 2010)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6044))

Abstract

Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie each cluster, changing the setup to biclustering. Furthermore, we make the indicators hierarchical, resulting in a hierarchy of progressively more specific biclusters. A non-parametric Bayesian formulation makes the model rigorous and yet flexible, and computations feasible. The formulation additionally offers a natural information retrieval relevance measure that allows relating samples in a principled manner. We show that the model outperforms other four biclustering procedures in a large miRNA data set. We also demonstrate the model’s added interpretability and information retrieval capability in a case study that highlights the potential and novel role of miR-224 in the association between melanoma and non-Hodgkin lymphoma. Software is publicly available.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Science, Helsinki Institute for Information Technology, Aalto University School of Science and Technology, P.O. Box 15400, FI-00076, Aalto, Finland

    José Caldas & Samuel Kaski

Authors
  1. José Caldas
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  2. Samuel Kaski
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Editors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 02139, Cambridge, MA, USA

    Bonnie Berger

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Caldas, J., Kaski, S. (2010). Hierarchical Generative Biclustering for MicroRNA Expression Analysis. In: Berger, B. (eds) Research in Computational Molecular Biology. RECOMB 2010. Lecture Notes in Computer Science(), vol 6044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12683-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-12683-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12682-6

  • Online ISBN: 978-3-642-12683-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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