One of the methods which support it is using graph grammars as a tool for representing the design process.
Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged.
The methods which support such a goal are to a large extent based on metadata (e.g., keyword, category, author, and other descriptors) that describe content and its properties.
All packages use object-oriented programming with classes and methods, which supports usability as well as integration with other R/Bioconductor packages [66], making it straightforward for a user to construct advanced analyses.
On the one hand, these results showed that mismatch position effect is relatively insensitive to experimental methods, which supports Affymetrix's decision to choose the 13th position when designing PM/MM probe pairs, (although our results showed that the position 12th may cause a slightly bigger loss of signal intensity in mismatches).
The stability of compound 9 in simulated vaginal fluid (SVF) was performed via HPLC-PDA method, which supported its utility for vaginal administration.
Stewart et al. described six interactive components which formed the basis of the patient-centred clinical method, which supported a comprehensive and holistic approach to care [ 31].
In this context, RAMSAS, a model-based method which supports the reliability analysis of systems through simulation by combining the benefits of popular OMG modeling languages with wide adopted simulation and analysis environments, has been recently proposed.
Present study revealed that the plant Argyreia nervosa possesses a significant anti-inflammatory activity as evidencesd in carrageenan induced paw edema method, which supports the folkloric claim of the anti-inflammatory activity of the plant.
Here we focus on what we call smart-data; a method which supports data engineering and knowledge engineering approaches that put greater emphasis on applying causal knowledge and real-world 'facts' to the process of model development, driven by what data are really required for prediction, rather than by what data are available.
