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To further elucidate the power of these computational approaches, through a case study of fat malabsorption, we demonstrate that heterogeneous but related data from diverse resources can be integrated and presented in an intuitive manner that in turn aids in further the understanding of the molecular basis of disease further.
We show that with the help of a systematic method that can efficiently combine data from diverse resources, it is possible to identify vulnerabilities that cover a considerable number of patients when aggregated across different cancer studies.
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Since its introduction a decade ago, DDDAS has been applied to a wide range of science and technology fields, with specific focus of areas requiring fast and reliable processing of massive data streams from diverse resources.
This knowledge is received from diverse resources such as the published literature.
It integrates the most up-to-date PPIs for human mitochondria from diverse resources.
The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases.
It is amenable to incorporating extant prioritization schemes and relevant data from diverse sources, thereby facilitating meta-analysis across resources from the U.S. EPA and elsewhere.
Emerging data collection technologies provide a multitude of rich data from diverse data sources.
The KNIME workflow proved a useful tool to merge data from diverse sources.
Errors can be created when data from diverse sources are combined.
The proposed models are compared with published data from diverse sources and agree well with the experimental data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com