Systems biology aims to integrate models and data from diverse
sources, and from different levels of description (from the molecular
level to cells, and organisms). Generating experimental
data, e.g.
from microarrays, and learning from data have been identified as
important tasks.
This talk will report on some
recently-proposed techniques that aim to use ontologies as a background
theory for learning, and will present a classification task and a gene
network search task (both applied to microarray data) where such a
theory would be useful.