Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7930
Title: Machine learning for multimodality genomic signal processing
Authors: Kung, SY
Mak, MW 
Issue Date: 2006
Source: IEEE signal processing magazine, 2006, v. 23, no. 3, p. 117-121 How to cite?
Journal: IEEE Signal Processing Magazine 
Abstract: Multiple modalities can be generated in various ways. One possiblity is via sensor diversity, and the other is feature diversity. In terms of sensor diversity, both the motif and gene expression modalities can be considered. Motifs are short sequences of DNA responsible for regulating gene networks and the expression of genes, whereas gene expression is the processing of producing proteins from information coded in genes. To further facilitate multi-modality fusion, a diversity of features may be extracted from each sensor by computational means. This is called feature diversity.
URI: http://hdl.handle.net/10397/7930
ISSN: 1053-5888
DOI: 10.1109/MSP.2006.1628886
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