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Proceedings Paper

Application of curvilinear component analysis to chaos game representation images of genome
Author(s): Joseph Vilain; Alain Giron; Djamel Brahmi; Patrick Deschavanne; Bernard Fertil
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Paper Abstract

Curvilinear component analysis (CCA) is performed by an original self-organized neural network, which provides a convenient approach for dimension reduction and data exploration. It consists in a non-linear, preserving distances projection of a set of quantizing vectors describing the input space. The CCA technique is applied to the analysis of CGR fractal images of DNA sequences from different species. The CGR method produces images where pixels represent frequency of small sequences of bases revealing nested patterns in DNA sequences.

Paper Details

Date Published: 9 March 1999
PDF: 9 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341111
Show Author Affiliations
Joseph Vilain, INSERM (France)
Alain Giron, INSERM (France)
Djamel Brahmi, INSERM (France)
Patrick Deschavanne, INSERM (France)
Bernard Fertil, INSERM (France)

Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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