The increasing amount of biological data being made available on-line,
combined with the already vast numbers of research papers available
electronically, makes Bioinformatics an exciting area for the
application of Artificial Intelligence techniques.
Now that many genome sequencing efforts are complete, the emphasis
is moving to interpreting the data. Gene expression networks, which
describe genes and their interaction, are one such means of explanation, and
on-going research in AIAI is applying Genetic Algorithms to
search the space of potential gene expression networks, and identify
the most promising. Early results are encouraging as experts judge the
networks as plausible.
Ontologies are already being used and developed
by biologists. The Gene
Ontology is the best known
example. Many anatomies of model species such as
mouse, drosophila and C Elegans are also being published. These
ontologies and anatomies are being used to index gene expression data.
Building on these resources the
XSPAN project is defining cross-species mappings which represent
the links between homologous tissues. Integrating these resources will
be a valuable contribution to the e-scientist exploring the
developmental links between species.
The XSPAN project has produced the
COBrA Ontology Browser, which is an ontology browser and editor for
GO and OBO ontologies. COBrA has been specifically designed to be usable
by biologists to create links between ontologies, and has the
COBrA was developed by Roman Korf. The modelling of is_a and
part_of in anatomy ontologies is also being studied, and
results are presented in this
More technical details of the OWL and RDF Schema can be
- allows drag-and-drop editing of GO ontologies
- supports translation to OWL and other Semantic Web languages
- supports the manual creation of mapings between terms in two
The retrieval and analysis of scientific texts is an important
service. Current keyword-based approaches are limited, and new
techniques are needed to generate mark-up in a machine interpretable
form (in RDF, for example). In recent research, Inductive Logic
Programming has been applied to learn information extraction rules
which locate instances of ontology relations in texts.
This supervised learning approach requires only a small set of
annotated texts to generalise from. Further details can be found in