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In this situation, the value of knowledge gained from analyzed data is restricted to the specific study in which these data were generated, whereas this knowledge could be leveraged during analysis of other studies.
Design of experiments (DOE) originated as a method to maximize the knowledge gained from experimental data.
However, this process should not discard the knowledge gained from past monitored data and should allow the updating of prediction functions incorporating this knowledge.
These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data?
The best approach is therefore to merge knowledge from data analysis (quantitative) with knowledge gained from experts of different areas (qualitative).
Knowledge gained from cis-elements, GO annotation and expression data will enable effective functional prediction of the identified TFs.
The value of big data comes from the knowledge gained from it and what you do with it.
Knowledge gained from bioterrorism research will improve public health.
Can we combine the knowledge gained from different calibration approaches?
There's no substitute for knowledge gained from experience.
really using the knowledge gained from EU-research project.
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