- Article -
| In Silico Biology 4, 0018 (2004); ©2004, Bioinformation Systems e.V. |
Classification of short kinetics by shape
Hans B. Sieburg1,* and Christa E. Müller-Sieburg2
1Department of Mathematics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093
Email: hsieburg@skcc.org
2Sidney Kimmel Cancer Center, 10835 Altman Row, San Diego, CA 92121
Email: cmuller@skcc.org
* corresponding author
Edited by N. Kolchanov; received January 11, 2004; accepted January 09, 2004; published March 06, 2004
Abstract
Discerning significant relationships in small data sets remains challenging. We introduce here the Hamming distance matrix and show that it is a quantitative classifier of similarities among short time-series. Its elements are derived by computing a modified form of the Hamming distance of pairs of symbol sequences obtained from the original data sets. The values from the Hamming distance matrix are then amenable to statistical analysis. Examples from stem cell research are presented to illustrate different aspects of the method. The approach is likely to have applications in many fields.
Key words: symbolic data transformation, Hamming distance, kinetics, hematopoietic stem cells, trading volume