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Towards the Discovery of Reliable Biomarkers from Gene-Expression Profiles: An Iterative Constraint Satisfaction Learning Approach

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Book cover Artificial Intelligence: Theories, Models and Applications (SETN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6040))

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Abstract

The article demonstrates the use of Multiple Iterative Constraint Satisfaction Learning (MICSL) process in inducing gene-markers from microarray gene-expression profiles. MICSL adopts a supervised learning from examples framework and proceeds by optimizing an evolving zero-one optimization model with constraints. After a data discretization pre-processing step, each example sample is transformed into a corresponding constraint. Extra constraints are added to guarantee mutual-exclusiveness between gene (feature) and assigned phenotype (class) values. The objective function corresponds to the learning outcome and strives to minimize use of genes by following an iterative constraint-satisfaction mode that finds solutions of increasing complexity. Standard (c4.5-like) pruning and rule-simplification processes are also incorporated. MICSL is applied on several well-known microarray datasets and exhibits very good performance that outperforms other established algorithms, providing evidence that the approach is suited for the discovery of biomarkers from microarray experiments. Implications of the approach in the biomedical informatics domain are also discussed.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), N. Plastira 100, Vassilika Vouton, Heraklion, 700 13, Crete, Greece

    George Potamias, Lefteris Koumakis, Alexandros Kanterakis & Vassilis Moustakis

  2. Department of Production and Management, Management Systems Laboratory, Technical University of Crete, Chania, 73100, Greece

    Lefteris Koumakis, Alexandros Kanterakis & Vassilis Moustakis

Authors
  1. George Potamias
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  2. Lefteris Koumakis
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  3. Alexandros Kanterakis
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  4. Vassilis Moustakis
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Editor information

Editors and Affiliations

  1. Institute of Informatics and Telecommunications, NCSR Demokritos, Ag. Paraskevi, 15310, Athens, Greece

    Stasinos Konstantopoulos , Stavros Perantonis , Vangelis Karkaletsis  & Constantine D. Spyropoulos , ,  & 

  2. Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Samos, Greece

    George Vouros

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Potamias, G., Koumakis, L., Kanterakis, A., Moustakis, V. (2010). Towards the Discovery of Reliable Biomarkers from Gene-Expression Profiles: An Iterative Constraint Satisfaction Learning Approach. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_27

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  • DOI: https://doi.org/10.1007/978-3-642-12842-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12841-7

  • Online ISBN: 978-3-642-12842-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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