Merja Oja, Janne Nikkilä, Petri Törönen, Eero
Castrén, and Samuel Kaski.
Learning metrics for visualizing gene functional similarities.
In Pekka Ala-Siuru and Samuel Kaski, editors, STeP 2002 - Intelligence, The Art of Natural and Artificial. The 10th Finnish Artificial Intelligence Conference, Oulu, Finland 15-17 Dec. 2002, pages 31-40, 2002.
(postscript, gzipped postscript)
The usual first step in analyzing the large and high-dimensional
data sets measured by microarrays is visual exploration. In this work
self-organizing maps have been used to visualize similarity
relationships of data samples. In all unsupervised data analysis methods the
measure of similarity determines the result; we propose to use the
learning metrics principle to derive a metric from interrelationships
between data sets.
A metric is derived for a gene knock-out expression data set by
considering those changes in the expression space that cause changes
in the functional classes of the genes to be more important. The genes for
which the new metric is the most different from the usual correlation
metric are listed and visualized with a self-organizing map computed
in the new metric.
Merja Oja
Last modified: Wed Mar 9 08:40:22 EET 2005