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Several combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have emerged as a promising tool for better detecting gene-gene (G × G) and gene-environment (G × E) interactions.
Label-free optical biosensors based on the resonant waveguide grating method have emerged over the last two decades as powerful tools that are capable of detecting the interactions of proteins and genes.
Recently however, concerns about the safety of this method have emerged because of the detection of asymptomatic cerebral microemboli, which were more prevalent in patients with duty-cycled RFA (37 and 38%) compared with irrigated RFA (7.4 and 8.3%) and cryoenergy (4.3 and 5.6%).
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A new hybrid learning method has emerged.
Recently, the stochastic sparse grid collocation method has emerged as an attractive alternative to SSFEM.
Consequently, application of serious games as a promising learning method has emerged in manufacturing education.
Most scientists have followed the first approach so far, and the second method has emerged as a versatile approach for combinatorial biomaterials discovery.
However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps.
The surface plasmon resonance (SPR) biosensor method has emerged as a very flexible and powerful approach for detecting a wide diversity of biomolecular interactions.
In terms of the design process, so far no method has emerged for systematically reaching an optimal solution from all aspects, although this is possible in principle.
With the increase of available computational resources in the past decade, CFD DEM (computational fluid dynamics discrete element method) has emerged as a viable high-fidelity method for the study of sediment transport.
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