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In this article, we present a novel mathematical methodology integrating gene/protein expression data into metabolic networks with the objective of finding key pathways for the particular phenotype under study.
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The proposed methodology integrates a great variety of mathematical tools including copula functions, MCS method, hydrological and hydraulic models, NSGA-II algorithm as well as ANN and fuzzy set theory.
The methodology integrates exergoeconomics with a professional process simulator, and represents an alternative to conventional mathematical optimization techniques, because it reduces substantially the number of variables to be considered in the improvement process.
The proposed LOD methodology integrates into ANN's learning process.
The methodology integrates experimental data obtained through on site sampling and measuring together with historical information.
In an earlier paper of our group, a new mathematical methodology to reduce the combinatorial complexity of integrating design and control was introduced (Malcolm et al., 2007).
"The algorithm within the online assessment reviews signs of non-natural growth of skin lesions and is based on an established mathematical methodology in biology called fractal geometry.
As for the mathematical methodology, we articulated it as a discrete bilevel mathematical programing.
It could be seen as an implementation to the existing mathematical methodology.
If, as a matter of mathematical methodology, you leave the odd numbers out, you are not going to end up with odd numbers.
The authors' statistical and mathematical methodology is complex.
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