When a diagnosis is made, an investigation is conducted into the nature of a problem to identify the treatment indicated.
Thirdly, policymakers may find themselves in a situation where they will have more open, strategic opportunities in which they are able to define a problem, to identify options, to characterise the costs of the options and their consequences, and to look for windows of opportunity to undertake preferred actions.
A substantial technical problem to identifying such sets is the astronomical number of possible combinations (triples, quadruples, and so forth) of reactions, which renders an exhaustive evaluation infeasible.
Although several cancer gene signatures have shown prognostic significance in a variety of cancers, it remains a critical problem to identify key genes as candidate therapeutic targets.
However, for (n> 3) and (m> 4), it is a hard problem to identify the positive definiteness of such a multivariate form.
However, it is possible, for a given problem, to identify algorithms that consistently produce better results compared with those results produced by other algorithms.
In light of these findings, it is a tantalizing problem to identify other sequence signals to help us identify novel CRMs that do not rely on these fallible sources.
Although a variety of gene signatures associated with metastasis or poor prognosis have been identified in various cancer types, it remains a critical problem to identify key genes as candidate therapeutic targets in metastatic or recurrent cancer.
Therefore, it remained as an important problem to identify the global structures of a given large data by seeking the underlying connectivity among the singular points or singular fibers.
Finite element model updating is an optimization problem to identify and correct uncertain modeling parameters.
