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Processing and extracting meaningful knowledge from count data is an important problem in data mining.
However, efficient bioinformatics tools are needed to extract meaningful knowledge from the enormous amount of data produced.
At this time, digging out meaningful knowledge from the big biological data network cannot be easily achieved using the above methods.
With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set.
It is not news that science produces an enormous literature [ 1] presently 23 million citations in MEDLINE® alone and that computational means such as text mining (TM) are needed to extract meaningful knowledge from it.
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Despite its limitations, the COSMIC database is a valuable resource that brings together findings from thousands of studies; our methods demonstrate the extraction of meaningful knowledge representations from this resource.
The Partnerships in Health System Improvement model of conducting research (http://www.cihr-irsc.gc.ca/e/34348.html) requires close cooperation between researchers and decision-makers from the genesis of the idea through to meaningful knowledge translation of the results.
To construct a globally meaningful knowledge after each mining algorithm finds its local model, the local model from each computer node has to be aggregated and integrated into a final model to represent the complete knowledge.
Independently from all of these limitations, acoustic segments still contain signals for seabed changes that, if appropriate procedures are established, can be translated into meaningful knowledge.
It is unavoidable, for otherwise no meaningful knowledge can be attained.
High-level content refers to the concrete co-construction of meaningful knowledge and learning.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com