Specimen preparation, microarray hybridization and data analysis methods were performed as described above.
To explore the consistency of the data, mass balances (see Methods) were performed on the data averages.
All procedures were approved by the Queen's University Belfast, School of Psychology Research Ethics Committee (45 2013) before any data was collected; all methods were performed in accordance with the relevant guidelines and regulations.
Data analysis, normalization and statistical methods were performed described for the mRNA expression analysis.
The model was developed by E.M. Statistical analysis of experimental data through Bayesian inference methods was performed by V.V. Simulations were carried out by E.M. and R.C.
Performance evaluation on prediction methods is performed with benchmark data sets, whereas comparative results show that the Nu-SVR model has high prediction accuracy, and the combinational optimization algorithm can improve prediction performance efficiently; hence these two methods are suitable for online monitoring and prediction system.
This is because of the stage at which the non-linear RMS calculation, which was required to combine data within each planar gradiometer pair (see Methods), was performed.
Secondary analyses using a longer episode-free run-in period, different control chart types, different time frames to evaluate diagnostic accuracy parameters, lower thresholds defining episodes of either type, and missing data methods were also performed.
In 2015, an optimization of redundant measurements location for thermal capacity of power unit steam boiler using data reconciliation method is performed [10].
Following feature selection, quadratic discriminant analysis (QDA), a multivariate data analysis method, was performed to classify the different groups under the assumption that each feature is normally distributed.
