While mashup tools mainly focus on modeling the data flow and rapid development, model-driven engineering approaches permit different views and more expressive modeling concepts.
We are looking to improve on this by using a slightly more expressive model, such as, non-linear SVM model.
We believe a more expressive model that is able to model such interactions between coverage terms would improve on the current results.
To provide protein engineers and scientists with comprehensive information and a more expressive model, we populate our domain ontology with the extracted information, which can then be queried as a knowledge base.
Though the propositional version of the logic is quite expressive, we augment it with temporal operators, and we outline an axiomatic system for this more expressive framework.
Motivation: There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources.
In what follows we attempt to minimise both difficulties by (a) making use of more expressive language models and (b) by making use of a larger training corpus.
Current modeling frameworks, especially in the protein protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models.
Nevertheless, most of these tools, especially in the protein protein interaction domain, have been limited to pure topological analysis of the pertaining networks, calling for stronger and more expressive network models (Huang and Fraenkel, 2009; Yeger-Lotem et al., 2009; Yosef et al., 2009).
