Hierarchical clustering combining numerical and biological similarities for gene expression data classification

Consultar RECERCAT

Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/2117/86695

Título:	Hierarchical clustering combining numerical and biological similarities for gene expression data classification
Autor/a:	Bosio, Mattia; Salembier Clairon, Philippe Jean; Bellot Pujalte, Pau; Oliveras Vergés, Albert
Otros autores:	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
Abstract:	High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació -Algorithms -Bioinformatics -Algorithm design and analysis -Bioinformatics -Clustering algorithms -Databases -Genomics -Prediction algorithms -Algorismes -Bioinformàtica
Derechos:
Tipo de documento:	Artículo - Versión publicada Objeto de conferencia
Editor:	Institute of Electrical and Electronics Engineers (IEEE)
Compartir: