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2016 | OriginalPaper | Chapter

Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data

Authors : Francisco Javier Gil-Cumbreras, Raúl Giráldez, Jesús S. Aguilar-Ruiz

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

In this work, we have extended the experimental analysis about an encoding approach for evolutionary-based algorithms proposed in [1], called probabilistic encoding. The potential of this encoding for complex problems is huge, as candidate solutions represent regions, instead of points, of the search space. We have tested in the context of gene expression biclustering problem, in a selection of a well-known expression matrix datasets. The results obtained for the experimental analysis reveals a satisfactory performance in comparison with other evolutionary-based algorithms, and a high exploration power in very large search spaces.

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Footnotes
1
In [1], IEPR was named as MOBPEOC (Multi-Objective Biclustering with Probabilistic Encoding and Overlapping Control).
 
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Metadata
Title
Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data
Authors
Francisco Javier Gil-Cumbreras
Raúl Giráldez
Jesús S. Aguilar-Ruiz
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_59

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