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Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm

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Artificial Immune Systems (ICARIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7597))

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Abstract

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. Unfortunately, this fascinating and classic area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we highlight: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (C) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the Literature for stability-based model selection methods.

An extended version of this manuscript appears in [20] and it is presented here as invited contribution to Bio- & Immune- Inspired Algorithms and Models for Multi-Level Complex Systems Workshop within ICARIS 2012.

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Authors and Affiliations

  1. Dipartimento di Matematica ed Informatica, University of Palermo, Via Archirafi 34, 90123, Palermo, Italy

    Raffaele Giancarlo

  2. Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    Filippo Utro

Authors
  1. Raffaele Giancarlo
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  2. Filippo Utro
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Editors and Affiliations

  1. Departmento de Computación, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. Insituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, 07300, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG8 1BB, Nottingham, UK

    Julie Greensmith

  3. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG81BB, Nottingham, UK

    Natalio Krasnogor

  4. Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, UK

    Pietro Liò

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Mario Pavone  & 

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Giancarlo, R., Utro, F. (2012). Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_20

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  • DOI: https://doi.org/10.1007/978-3-642-33757-4_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33756-7

  • Online ISBN: 978-3-642-33757-4

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