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Complex research problems address societal issues involving multiple, interacting influences with no known solution or established methods and expertise for studying the interrelated contexts in which they take place.
Prisoners are a particularly vulnerable group who have historically been included as research subjects but have also been exploited by investigators in pursuit of useful solutions to complex research problems (Gostin, Vanchieri, and Pope 2007; Hornblum 1997, 1998; Mitford 1974).
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It is also coupled with high time costs causing the engineering task to be modified into a complex research problem.
Of course, multiple adaptive mechanisms operating at different time scales and in different layers can affect each other and would constitute a complex research problem on its own.
Some of these challenging statistical issues in medical and clinical research (with emphasis on clinical trials) are appraised in the light of existing statistical tools, which are available for less complex clinical research problems.
Having a common interest of progressing toward more complex biologically relevant research problems, it is important that we are able to provide carefully prepared training and test data collections, and tools to access them, to facilitate research.
Prediction methods based on SVM have been successfully exploited in many research problems involving complex, sequence or biological datasets [20] [22], like remote protein similarity detection [23], DNA methylation status [24], protein domains [25] and multiclass cancer diagnosis [26].
A series of important research problems have resulted from the complex nature of networks.
Indeed, research problems in this field are inherently too complex to be addressed by single disciplines.
Most research problems in epidemiology are multifaceted and, therefore, complex.
The complex nature of the networks has resulted in a series of important research problems.
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