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

Author
Bosio, M.; Salembier, P.; Bellot, P.; Oliveras, A.
Type of activity
Presentation of work at congresses
Name of edition
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date of publication
2013
Presentation's date
2013-07
Book of congress proceedings
Conference proceedings : 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
First page
584
Last page
587
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/EMBC.2013.6609567 Open in new window
Repository
http://hdl.handle.net/2117/86695 Open in new window
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6609567 Open in new window
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.
Citation
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.
Keywords
Algorithm design and analysis, Bioinformatics, Clustering algorithms, Databases, Genomics, Prediction algorithms
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants