Knowledge-based classification

An influential classification of the different types of information processing involved in industrial tasks was developed by J. Rasmussen of the Rise Laboratory in Denmark. This scheme provides a useful framework for identifying the types of error likely to occur in different operational situations, or within different aspects of the same task where different types of information processing demands on the individual may occur. The classification system, known as the skill-, rule-, knowledge-based (SRK) approach is described in a... 

In Figure 2.6, the slips/mistakes distinction is further elaborated by relating it to the Rasmussen SRK classification of performance discussed earlier. Slips can be described as being due to misapplied competence because they are examples of the highly skilled, well practiced activities that are characteristic of the skill-based mode. Mistakes, on the other hand, are largely confined to the rule and knowledge-based domains. 

Perform task analysis (see Chapter 4) and identify skill, rule or knowledge-based tasks or aspects of tasks (the flow diagram in Figure 2.7 may be used to assist in this classification). 

Dubus, E., Ijjaali, I., Petitet, F. and Michel, A. (2006) In sUico classification of HERG channel blockers a knowledge-based strategy. ChemMedChem, 1, 622-630. 

Mestres J. (2004) Computational chemogenomic approaches to systematic knowledge-based drug discovery. Curr. Top. Drug Discov. Dev. 7 304—313. National Center for Health Statistics (2005) The International classification of diseases, 9th revision, chnical modification. 1CD-9-CM, 6th ed. http //www.cdc.gov/nchs/icd9.htm. 

Most electrochemical studies carried out today make use of online computers for control of experiments and for data acquisition and analysis, including the techniques described earlier. Examples of the application of computer evaluation of experimental results include, for instance, pattern recognition [151] and the recording of current-time profiles of the form A(lni)/A(lnt) versus t for mechanistic classification [152] as well as nonlinear regression techniques [153-155]. Efforts have also been made to use knowledge-based systems for the elucidation of reaction mechanisms [156]. 

The objective of this chapter is to provide comprehensive knowledge-based information by critical analysis, classification, model description, and applications of a variety of ELM separation processes. New perspectives and directions of development in these fields are also discussed. 

Activity prediction can be trained in Apex-3D on the basis of identified biophores to provide the best estimation for a particular type of compound. Activities for new molecules are then predicted from their own dynamically created training set. Predictions can be made as classification of the new compound into predefined classes or by calculating a quantitative value based on 3D QSAR models present in the knowledge base. 

Hoppe, C., Steinbeck, C. K, and Wohl-fahrt, G. (2006) Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials. J. Mol. Graph. Model. 24, 328-340. 

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