Abstract
In the current post-genomic era, various aspects of gene function are being uncovered by a large number of experiments producing huge amounts of heterogeneous data at an accelerating pace. Putting all this data together, while taking into account existing knowledge has become a pressing need for developing environments able to explore and simulate biological entities at a system level.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Badea, L.: Functional discrimination of gene expression patterns in terms of the Gene Ontology, Proc. of the Pacific Symposium on Biocomputing PSB-2003.
Cooper, G. F. and C. Yoo, Causal discovery from a mixture of experimental and observational data, Proceedings of the Conference on Uncertainty in Artificial Intelligence (1999) 116–125.
Fruewirth T. Theory and Practice of Constraint Handling Rules, JLP 37:95–138, 1998.
Gene Ontology: tool for the unification of biology. Nature Genet. 25: 25–29, 2000.
Ian Horrocks et al. Reviewing the design of DAML+OIL: An ontology lan-guage for the semantic web. AAAI 2002 http://www.daml.org
Hughes et al.Functional discovery via a compendium of expression profiles.Cell 102(1):109–26, 2000
Nienhuys-Cheng, S. H., R. de Wolf. Foundations of Inductive Logic Program-ming, Springer, 1997.
Ogata, H., Goto, S., Fujibuchi, W., Kanehisa, M.: Computation with the KEGG patway database, BioSystems, 47, 119–128(1998), http://www.genome.ad.jp/kegg/kegg2.html
Pe’er, D., Regev, A., Elidan, G., Friedman, N.: Inferring Subnetworks from Perturbed Expression Profiles, Bioinformatics, Vol.1, no.1 2001, pages1–9.
Pearl, J., Verma, T. S.: “A theory of inferred causation”, Proc. KR-91, 441–452.
Spirtes, P., Meek, C., and Richardson, T. Causal inference in the presence of latent variables and selection bias. In Proceedings of UAI-95, pp. 499–506.
Spirtes, P.: ‘Directed cyclic graphical representation of feedback models’, Proceedings of the 11th Conference on Uncertainty in Articial Intelligence, Montreal QU: Morgan Kaufmann, pages 491–498.
Wingender, E.: The TRANSFAC System on Gene Regulation, Trends in Glycoscience and Glycotechnology 12, 255–264 (2000), http://transfac.gbf.de/TRANSFAC/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Badea, L., Tilivea, D. (2003). Integrating Biological Process Modelling with Gene Expression Data and Ontologies for Functional Genomics (Position Paper). In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2003. Lecture Notes in Computer Science, vol 2602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36481-1_27
Download citation
DOI: https://doi.org/10.1007/3-540-36481-1_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00605-3
Online ISBN: 978-3-540-36481-8
eBook Packages: Springer Book Archive