Construction algorithms comparative results

Standardizing the spectral response is mathematically more complex than standardizing the calibration models but provides better results as it allows slight spectral differences - the most common between very similar instruments - to be corrected via simple calculations. More marked differences can be accommodated with more complex and specific algorithms. This approach compares spectra recorded on different instruments, which are used to derive a mathematical equation, allowing their spectral response to be mutually correlated. The equation is then used to correct the new spectra recorded on the slave, which are thus made more similar to those obtained with the master. The simplest methods used in this context are of the univariate type, which correlate each wavelength in two spectra in a direct, simple manner. These methods, however, are only effective with very simple spectral differences. On the other hand, multivariate methods allow the construction of matrices correlating bodies of spectra recorded on different instruments for the above-described purpose. The most frequent choice in this context is piecewise direct standardization... 

An automated FTP service was used to obtain the predictions for all of our 168 integral membrane proteins by using the Rost et al. method [9]. A total of 11870 residues were correctly predicted in the TMH conformations, 2436 residues were overpredicted, 2512 residues were underpredicted, while 50335 residues were correctly predicted not to be in the TMH conformation. One of many different performance parameter that can be constructed by using these data is the Aj] parameter (Methods). Its value is A m = 0.656, which is inferior to our value of 0.712 (Table 9) for the same parameter. However, when tested on the subset of 63 proteins used by Rost et al. [9] the Ajj parameter, calculated from predictions returned by automated service, becomes 0.733, which is comparable to our value of Ajj = 0.740 for the same subset of proteins (Table 9). Similar test on the subset of 105 proteins, never before seen in the training process for the neural network algorithm, gave quite a low value of Ajj = 0.610 for the Rost et al. method [9]. That value is lower than our value of Ajj = 0.682 for the same subset of 105 proteins (Table 9). All of 63 proteins selected by Rost et al. [9] are also predicted as membrane proteins, but their method does not recognize 2 out of 105 membrane proteins selected by us. Underprediction of membrane proteins is due to serious underprediction of transmembrane helices 50 of observed 419 TMH are underpredicted and 11 overpredicted by Rost et al. [9]. For comparison our Table 9 results (row f) for Aj are obtained for the case of 21 underpredicted and 25 overpredicted TMH in the same test set of 105 proteins. 

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