Before becoming more expert in problem solving, learners progress through several transitional stages characterized by different knowledge structures: elaborated causal networks, abridged networks, illness scripts, and instance scripts [ 18].
However, when coexpression networks were enriched with the different knowledge sources mentioned above, the networks highlighted key genes impacted by the biological transformation.
As more human disease phenotype data become available, our approach can be further improved by integrating new disease phenotype networks, given that the new networks contain different knowledge.
Note that our approach can easily incorporate more phenotypic or genetic networks in the same way, given that the new networks contain different knowledge from the existing ones.
In distributed network environment, different knowledge can be connected together for participants to share, becoming effective.
Keys to here are diversity, heterogeneity, and pluralism of different knowledge and innovation modes, and their linking together via an architecture of co-evolving networks.
By establishing the idea of trilateral networks between research, industries, and government, this model emphasized the coexistence and coevolution of different knowledge sources.
Results indicate that the knowledge networks and the technology-based collaboration networks in the nano-energy field are decoupled and that they have different degrees of integration.
Several different knowledge representation schemes are already in use: decision trees, first-order logic expert systems, calculations (mathematical modeling), trained neural network simulations, ….
