Automating experiments using semantic data in a bioinformatics grid

• Published in: IEEE intelligent systems Vol. 19; no. 1; pp. 48 - 55
• Main Authors: Wroe, C., Goble, C., Greenwood, M., Lord, P., Miles, S., Papay, J., Payne, T., Moreau, L.
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01.01.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Bioinformatics; Computation; Computational modeling; Computer simulation; Conduction; Distributed processing; Genetics; Human factors; Intellectual property; Knowledge management; Laboratories; Mathematical analysis; Mathematical models; Physics computing; Scalability; Semantics; Web services; Workflow
• ISSN: 1541-1672; 1941-1294
• DOI: 10.1109/MIS.2004.1265885

The transition from laboratory science to in silico e-science has facilitated a paradigmatic shift in the way we conduct modern science. We can use computationally based analytical models to simulate and investigate scientific questions such as those posed by high-energy physics and bioinformatics, yielding high-quality results and discoveries at an unprecedented rate. However, while experimental media have changed, the scientific methodologies and processes we choose for conducting experiments are still relevant. As in the lab environment, experimental methodology requires samples to undergo several processing stages. The staging of operations is what constitutes the in silico experimental process. The use of workflows formalizes earlier ad hoc approaches for representing experimental methodology. We can represent the stages of in silico experiments formally as a set of services to invoke.