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For the purpose of evaluation of the five methods employed, a test dataset of Pfam families with known structure and SCOP domain information was generated.
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Statistical methods employed an intention to treat and repeated-measures design.
Most known separation methods employ a column chromatography technique [19].
The method employed by RN corresponds to the first step of C3NET but also of ARACNE, however, both methods employ a second step.
These methods employ a range of analytical techniques that directly affect both the sensitivity and selectivity of the analysis.
Current network inference methods employ a number of different strategies to reduce the search space of network structure solutions.
Filter methods employ a criterion to evaluate each feature individually and is independent of the model [ 10].
However, all these methods employ a single type of phenotypic information (i.e. transcription data), whereas post-transcriptional regulation has a recognized and substantial effect on a phenotype.
Most methods employ a statistical approach to identify interacting marker pairs based on deviation from a null distribution and estimation of type I error.
The method employed a combination of two types of HPLC analyses.
The optimized method employed a fused-silica capillary of 50 μm i.d.
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