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Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.

Authors :
Wang H
Azuaje F
Black N
Source :
IEEE transactions on nanobioscience [IEEE Trans Nanobioscience] 2002 Dec; Vol. 1 (4), pp. 146-66.
Publication Year :
2002

Abstract

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.

Details

Language :
English
ISSN :
1536-1241
Volume :
1
Issue :
4
Database :
MEDLINE
Journal :
IEEE transactions on nanobioscience
Publication Type :
Academic Journal
Accession number :
16689206
Full Text :
https://doi.org/10.1109/tnb.2003.809465