A further issue in summarizing data is assessing reliability across both different analytical methods and different laboratories.
Here we examine both methods and their application to different areas in computer-aided drug design, ranging from virtual screening to metabolism prediction.
Several subspace identification methods (both open loop and closed loop methods), and different simulated data sets ranging from ideal 2 × 2 linear systems, to a fairly realistic nonlinear debutanizer process simulator, are considered.
All subjects used both methods and both cases but in different combinations according to the randomized schema (see more information in the section Statistical methods, below).
Various studies, using different methods and different sets of data, have reached widely varying conclusions.
Detailed presentation and derivation of both the MPS method and different spring network models is given.
Since the crimping procedures in both methods are different (and hence different crimped configurations), we are effectively comparing the difference in crimping as well.
Closed-form Lévy-type solutions are obtained for the governing equations of both theories using separation of variables method and different types of classical boundary conditions, namely simply-supported, clamped and free edge, are exactly satisfied.
The results presented in this paper and those available in previous literature establish the extensive validity of the application of supervised learning methods for species classification with DNA Barcodes, testing both the accuracy of different methods and of different dataset types.
This implies that both methods, although different in scope and sensitivity, offer highly consistent results.
