Expand knowledge on disease related changes in glycosylation patterns and its integration in genome and proteome data provides new basic biomedical insights and thus far reaching possibilities for diagnostic application (prevention, cure and progress of treatment follow up) as well as for new therapeutic interventions. Development of new computational tools empowers the Omics research. We discuss here application of computational tools in experiment optimization for generation of robust data where and when applicable, and mining of the data in curetted database. We also present a robust data analysis platform driven by database management systems to perform bi-directional data processing-to-domain identification with declarative querying capabilities. Lastly, we illustrate Machine Learning Methods as predictive models for the analysis of biomedical experimentation and data Integration, warehousing and validation concepts.