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The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies.
The inverse problem of determining unknown coefficient in a linear parabolic equation by using over-measured data has generated increasing interest from engineers and scientists during the last few decades.
The inverse problem of determining an unknown coefficient in a linear parabolic equation by using over-measured data has generated an increasing amount of interest from engineers and scientist during the last few decades.
The inverse problem of unknown source function in a linear inhomogeneous parabolic equation by using over-measured data has generated an increasing amount of interest from engineers and scientist during the last few decades.
The inverse problem of determining parameter in a linear parabolic equation by using over-measured data has generated an increasing interest from engineers and scientist in recent years because such problems play a crucial role in engineering, physics and applied mathematics.
The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment.
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These contradictory data have generated another controversy regarding the mechanisms of enhanced thermal conductivity in nanofluids.
These data have generated considerable controversy.
Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer.
Our in silico analyses, along with current and previous functional data, have generated a comprehensive overview of the ER stress response in soybean.
Careful examination of genetic databases, pre-clinical and clinical data have generated a comprehensive tiering system which ensures that only oncogenically pertinent abnormalities are actioned.
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