Making the equivalence explicit this way helps to illustrate the provenance of data integration efforts.
Data integration efforts are legion, but even in the rare instances where they succeed, they are found to be insufficient to advance programs because interpretation of query results becomes a research project in itself.
Previous web-based data integration efforts have focused almost exclusively on the logical level of data models, creating a need for techniques that focus on the conceptual level in order to communicate the structure and properties of the available data to users at a higher level of abstraction.
Thus, network-based data integration efforts are inevitably resulting in an artifact: Network diagrams attempt to display processes that do neither occur at the same time, nor on the same time scale, nor at the same place, nor for the same cell line.
The proposed quantitative noise analysis method may also prove useful for other data integration efforts.
Subsequently we explain how ontologies can be exploited to facilitate information sharing and data integration efforts for bioinformatics with reference to real-world, large-scale biological information portals, namely the cancer Biomedical Informatics Grid (caBIG), and WikiProteins, a proprietary knowledge commons for proteins.
In addition, data integration efforts to answer synthetic research questions have been conducted in the last few decades by empirical ecologists performing secondary or meta-analyses of ecological processes (e.g., [ 19– 23]), and by researchers in working groups at national synthesis centers in the US and other countries producing new knowledge through synthesis [ 4, 24– 27].
Goals of such data integration efforts are (1) to gain a better understanding of the observable phenotypes of the cell, (2) to predict potential functions of molecular signatures, and (3) to apply these in silico models for biological discovery and engineering applications.
