Domains identification

As the uses of toxicological-based quantitative structure-activity relationships (QSARs) move into the arenas of priority setting, risk assessment, and chemical classification and labeling the demands for a better understanding of the foundations of these QSARs are increasing. Specifically, issues of quality, transparency, domain identification, and validation have been recognized as topics of particular interest (Schultz and Cronin, 2003). 

In this present analysis concerns about quality, transparency, and domain identification are addressed in the validation of a previous developed QSAR. This QSAR examines the prediction of ectotoxic potency for population growth impairment to the aquatic ciliate Tetrahymena pyriformis by substituted benzenes. 

Central to the issues of quality, transparency, and domain identification as they relate to toxicological QSAR is biological data. High quality toxicity data on a structurally diverse set of molecules are required to formulate and validate high quality QSARs. Quality toxicity data typically come from standardized assays measured in a consistent manner, with a clear and unambiguous endpoint, and low experimental error. In such cases, quality is associated with values, which are accurate, consistent with other data within the same set, and consistent with data for other similar endpoints. In the case of comparisons between endpoints, it is as important for data to be consistent between endpoints as for the inconsistencies to be consistent. 

Lappalainen, P., Watmough, N. J., Greenwood, C., and Saraste, M., 1995, Electron transfer between cytochrome c and the isolated Cu, domain identification of substrate-binding residues in cytochrome c oxidase. Biochemistry, 34 582495830. 

T. Osterlund, D.J. Beussman, K. Jule-nius, P.H. Poon, and S. Linse, Domain identification of hormone-sensitive lipase by circular dichroism and fluorescence spectroscopy, limited proteolysis, and mass spectroscopy, J. Biol. Chem., 1999, 274, 15382-15388. 

Murvai, J., K. Vlahovicek, and S. Pongor, A simple probabilistic scoring method for protein domain identification. Bioinformatics, 2000. 16(12) p. 1155-6. 

Goerge, R. A., and J. Heringa. 2002. Protein domain identification and improved sequence similarity searching using PSI-BLAST. Proteins 48 672-81. 

Here we review different strategies of domain identification at the sequence level from a historical perspective, point to some future directions of domain research, and describe domain discovery in the context of genome analysis. To support our points, we provide illustrative examples of domains that are mostly represented in SMART (Simple Modular Architecture Research Tool Schultz et al., 1998, 2000). Where a SMART domain name is mentioned in the text, we represent it in boldface. 

MyHits http //myhits.isb-sib.ch Protein annotation, domain identif. 

McVerry, G.H.(1979) Frequency Domain Identification of Structural Models from Earthquake Records. EERL Report No.79-02, California Institute of Technology, Pasadena, Calif., Oct., pp.213. 

In evaluating control systems for microrotorcrafts, Mettler [8] found that frequency domain identification served as a better method than time domain identification. This is because in frequency domain identification, the output measurement noise does not affect the results, it is possible to focus on a precise frequency range (which minimizes the disparity between the modes of motion), and frequency responses can completely describe the system s linear dynamics. Mettler also determined that the rigid body equations of motion needed to be expanded through use of the hybrid formulation to generate a more accurate control system. This method models the rotor motion using a tip-path plane model and expresses the rotor forces and moments in terms of the rotor states. The rotor and fuselage motions are then dynamically coupled. 

Molina FJ, Pegon P, Verzeletti G (1999) Time-domain identification frran seismic pseudodynamic test results on civil engineering specimens. In 2nd inlematiraial conference on identification in engineering systems, Swansea... 

How to identify and delineate these domains is still an open problem as discussed above. It is important to realize that the existing algorithms for domain identification do not always agree the corresponding discrepancies in domain definition translate into differences between structural classifications that do not share the same definition. 

Goberdhansingh, E., L. Wang W. R. Cluett (1992), Robust frequency domain identification . Chemical Engineering Science 47, 1989-1999. 

Using only the FFT data within a selected band also significantly reduces modeling error risk because the identification results are invariant to complexities in the excluded bands containing other modes not of interest or even dynamics that are difficult to model. The mechanical response, excitation, and channel noise are assumed to have a flat spectrum within the selected frequency band only, rather than over the whole sampling band from zero to the Nyquist frequency (half of the sampling frequency). The latter is inevitable in time-domain identification approaches. 

A system identification method is considered parametric if a mathematical dynamic model (often formulated in state-space) is realized in a first step and the dynamic properties of the system estimated from the realized model in the second step. Nonparametric system identification methods directly estimate the dynamic parameters of a system from transformation of data, e.g., Fourier transform or power-spectral density estimation. Time-domain identification methods estimate the dynamic parameters of a system by directly using the measured response time histories, while frequency-domain methods use the Fourier transformation or power-spectral density estimation of the measured time histories. There is also a class of time-frequency methods such as the short-time Fourier transform and the wavelet transform. These methods are commonly used for identification of time-varying systems in which the dynamic properties are time-variant Linear system identification methods are based mi the assumption that the system behaves linearly and... 

---