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Título:
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Microarray classification with hierarchical data representation and novel feature selection criteria
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Autor/a:
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Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Vergés, Albert
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
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Abstract:
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Microarray data classification is a challenging prob-
lem due to the high number of variables compared to the
small number of available samples. An effective methodology
to output a precise and reliable classifier is proposed in this
work as an improvement of the algorithm in [1]. It considers the
sample scarcity problem and the lack of data structure typical of
microarrays. Both problem are assessed by a two-step approach
applying hierarchical clustering to create new features called
metagenes and introducing a novel feature ranking criterion,
inside the wrapper feature selection task. The classification ability
has been evaluated on 4 publicly available datasets from
Micro
Array Quality Control study phase II
(MAQC) classified by 7
different endpoints. The global results have showed how the
proposed approach obtains better prediction accuracy than a
wide variety of state of the art alternatives |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
-Bioinformatics
-Biology -- Data processing
-Bioinformàtica
-Biologia -- Informàtica |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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Compartir:
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