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This convergence of deep sequencing technology and the potential regulatory roles of miRNA in stem cells provide many opportunities as well as challenges in terms of data mining methodology in uncovering biological significance.
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None of these assurances can be claimed of the data mining methodology we have implemented in this study.
As illustrated in this paper, this data mining methodology has the advantage of identification of genetic interactions between pathways involved in the aetiology of depression, in keeping with the etiological heterogeneity of this disorder (see later).
In this paper, a data mining methodology named SEMMAO is addressed, some corresponding data mining software tools and practical applications in Baosteel are also introduced.
Even sophisticated text mining methodology does not match the human brain in language interpretation.
In addition, their proposed data mining methodology is more proficient for identifying malware and behavioral classification of malware can be helpful to recognize malware in a behavioral antivirus.
In this work, we developed a data mining methodology to extract multidimensional sequential patterns to characterize temporal behaviors.
Likewise, our data mining methodology is generic enough to be applied over any data stored in the proposed DW.
We show that the approach presented in [2] can be regarded as a data mining methodology.
We have argued that the traffic forecasting methodology proposed in [2] can be regarded as a data mining methodology and can be adapted for WiMAX traffic prediction.
Figure 1 An overview of the text mining methodology.
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