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Krylov subspace methods (KSM) have proven to be powerful methods for solving sparse linear systems arising in several engineering problems and, more recently, in computational economics.
These tools can be powerful methods for identifying microbes, but only if the respective culture conditions, microscope sensitivity, serologic reagents, and PCR primers are compatible with the microbial target and specimen type.
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Fluxomics and metabolomics are powerful methods for the quantitative analysis of cell metabolism.
GWAS and eQTLs are powerful methods for identifying genetic variants contributing to disease risk and gene expression.
Classical in vitro biochemistry and biophysics approaches are powerful methods for hit identification and verification, and structural biology plays a key role in guiding chemists to design specific and potent inhibitors.
Analyses of character evolutionary history, such as ancestral state reconstruction (reviewed by [ 5]) and stochastic character mapping [ 6], are powerful methods for studying the origin and maintenance of phenotypic diversity [ 2, 3].
Zhang-gradient (ZG) method is a combination of Zhang dynamics (ZD) and gradient dynamics (GD) methods which are two powerful methods for online time-varying problems solving.
Both spectroscopic techniques are very powerful methods for characterizing chemically bonded organoligands of stationary phases.
SAGE and MPSS are two powerful methods for genome-wide expression profiling and novel gene identification [ 4, 5, 7].
In the scientific study of such systems, modelling and simulation are potentially powerful methods for uncovering the general principles that underlie such phenomena.
Overall, feedback based on intraoperative surgical performance was found to be a powerful method for improving performance.
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