Biclustering von Microarray-Genexpressionsdaten

Biclustering von Microarray-Genexpressionsdaten

Balamurugan Rengeswaran / N Annadasampalayam Mathaiyan / Premalatha Kandasamy

53,60 €
IVA incluido
Disponible
Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
ISBN:
9786209312090
53,60 €
IVA incluido
Disponible

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Sobre IBD

Die Extraktion aussagekräftiger Informationen aus Genexpressionsdaten stellt sowohl für Forscher im Bereich der Informatik als auch für Biologen eine große Herausforderung dar. Durch die Analyse von Genexpressionsdaten lassen sich Verhaltensmuster von Genen wie beispielsweise die Art ihrer Interaktion, die Ähnlichkeit ihres Verhaltens usw. bestimmen. Um verschiedene Muster aus Genexpressionsdaten zu identifizieren, sind Data-Mining-Techniken unerlässlich. Zu den wichtigsten Data-Mining-Techniken, die für die Analyse von Genexpressionsdaten angewendet werden können, gehören Clustering, Klassifizierung, Assoziationsregel-Mining usw. Clustering ist eine wichtige Data-Mining-Technik für die Analyse von Genexpressionsdaten. Allerdings hat Clustering auch einige Nachteile. Um die mit Clustering verbundenen Probleme zu überwinden, wurde Biclustering eingeführt.

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