Knowledge-based prediction identification

Degree of agreement between average predictions of a model or the average of measurements and the true value of the quantity being predicted or measured. Accuracy is also a criterion used to evaluate the knowledge base uncertainty. It focuses on the identification of the most important bottlenecks in the available knowledge and the determination of their impact on the quality of the result. 

In silico tools make a significant contribution to the SAR-based early identification of potential toxicity. An increasing volume of published preclinical and clinical toxicity data are collected and used to build structure-related searchable databases. These expert knowledge databases can analyze chemical structures and match them with potential mechanisms of toxicity. DEREK for Windows (Lhasa Ltd.)39 is one of such broadly used knowledge-based expert systems to provide toxicology alerts for new compounds. Although certainly not comprehensive, numerous efforts have been made to predict hepatotoxicity. Recently,... 

FIGURE 7.5 Metabolite identification strategy based on integration of knowledge-based metabolic predictions with liquid chromatography list-dependent tandem mass spectrometry. (Adapted from Reza Anari, M. et al., Anal. Chem., 76, 823, 2004. With permission.)... 

Algorithm for identification of pharmacodynamic drag interactions is based on comparison of biological activity spectra of the compounds (in the block diagram they are labeled as compound 1 and compound 2), predicted by PASS, together with information from the PharmaExpert knowledge base ( activity-activity relationships). Antagonistic pharmacodynamic effects are determined by ... 

The success of MPC is based on a number of factors. First, the technique requires neither state space models (and Riccati equations) nor transfer matrix models (and spectral factorization techniques) but utilizes the step or impulse response as a simple and intuitive process description. This nonpara-metric process description allows time delays and complex dynamics to be represented with equal ease. No advanced knowledge of modeling and identification techniques is necessary. Instead of the observer or state estimator of classic optimal control theory, a model of the process is employed directly in the algorithm to predict the future process outputs. 

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