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

Autor
Bosio, M.; Salembier, P.; Bellot, P.; Oliveras, A.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Any de l'edició
2013
Data de presentació
2013-07
Llibre d'actes
Conference proceedings : 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Pàgina inicial
584
Pàgina final
587
Editor
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/EMBC.2013.6609567 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/86695 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6609567 Obrir en finestra nova
Resum
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.
Citació
Bosio, M., Salembier, P., Bellot, P., Oliveras, A. Hierarchical clustering combining numerical and biological similarities for gene expression data classification. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "Conference proceedings : 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference". Osaka: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 584-587.
Paraules clau
Algorithm design and analysis, Bioinformatics, Clustering algorithms, Databases, Genomics, Prediction algorithms
Grup de recerca
GPI - Grup de Processament d'Imatge i Vídeo
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants