Author(s)

Hao Fan, Fei Wang

International School of Software, Wuhan University, Hubei, P.R. China.
International School of Software, Wuhan University, Hubei, P.R. China,.

In web environments, proteomics data integra-tionin the life sciences needs to handle the problem of data conflicts arising from the het-erogeneity of data resources and from incom-patibilities between the inputs and outputs of services used in the analysis of the resources. The integration of complex, fast changing bio-logical data repositories can be potentially sup-ported by Grid computing to enable distributed data analysis. This paper presents an approach addressing the data conflict problems of pro-teomics data integration. We describe a pro-posed proteomics data integration architecture, in which a heterogeneous data integration sys-tem interoperates with Web Services and query processing tools for the virtual and materialised integration of a number of proteomics resources, either locally or remotely. Finally, we discuss how the architecture can be further used for supporting data maintenance and analysis ac-tivities.

Evoked potentials (EPs), Alpha stable distribution, Blind source separation, Minimum dispersion (MD), Fractional lower order statistics (FLOS)

Fan, H. and Wang, F. (2008) Using schema transforation pathways for biological data integration. Journal of Biomedical Science and Engineering, 1, 204-209. doi: 10.4236/jbise.2008.13035.