Domain expertise

Today, the use of CHIRBASE as a tool in aiding the chemist in the identification of appropriate CSPs has produced impressive and valuable results. Although recent developments diminish the need for domain expertise, today the user must possess a certain level of knowledge of analytical chemistry and chiral chromatography. Nevertheless, further refinements will notably reduce this required level of expertise. Part of this effort will include the design of an expert system which will provide rule sets for each CSP in a given sample search context. The expert system will also be able to query the user about the specific requisites for each sample (scale, solubility, etc.) and generate rules which will indicate a ranked list of CSPs as well their most suitable experimental conditions (mobile phase, temperature, pH, etc.). 

In Chapter 43 the incorporation of expertise and experience in data analysis by means of expert systems is described. The knowledge acquisition bottleneck and the brittleness of domain expertise are, however, the major drawbacks in the development of expert systems. This has stimulated research on alternative techniques. Artificial neural networks (ANN) were first developed as a model of the human brain structure. The computerized version turned out to be suitable for performing tasks that are considered to be difficult to solve by classical techniques. 

We would typically attract perhaps 15-20 initial one-page descriptions, and most people would follow through and write a full business plan. However, a majority of these plans were very academic. You would get a beautifully prepared plan for a business with a market potential of S 100 million within live years, but you could tell that management did not have the domain expertise to follow through. They didn t have the experience needed to execute the plan, and didn t have a clear idea how to reach their revenue objectives. 

Structure representation and chemistry intelligence engine As described, commercial tools are available and there is no need to reinvent the wheel. Plus, developing these tools requires significant domain expertise and resources. However, influencing the tool vendors to continually improve their products is a good idea. 

It is important to point out that while most of the burden and expertise for impact are borne by the proposing researcher, it is often the chemists on the overall drug discovery team that have the appropriate domain expertise to evaluate the competitive landscape. It is also important for the assay development team to review and verify the assay development and optimization data of the benchtop assay. Sometimes the fundamental assumptions made are faulty, for example that the steady-state conditions are not met for an enzyme assay (i.e., nonlinear progress curves with too high a level of substrate consumption), that endpoint measurements were taken when a progress curve had already plateaued (this is a surprisingly common error, whose impact has been analyzed) [see Fi f. 3 in [4] for simulation, discussion of reasons for nonlinearity with respect to substrate depletion, and workable off-linearity for HTS], or that substrate concentration-to-7Cm ratio is set inappropriately and so will not allow detection of the desired types of inhibitors... 

The distributed type of the federated architecture benefits from the local domain expertise of scientists who maintain the data. It tends to be more practical, obviating the necessity to move large datasets around and immune to the synchronization problems affecting local solutions. The drawback of this solution is the dependency on the network quality, bandwidth, and uncontrolled network congestion. It is therefore critical that distributed systems are designed with particular care with respect to their scalability for large datasets. An additional benefit of the distributed system is its deployment flexibility. It can be configured to be used in either a distributed or local fashion, or in any combination of the two. 

Another emerging technology is Web 2.0, which is anticipated to enable a business revolution by using the Internet as a platform to help online collaboration among the people, which is referred to as social computing, through the Internet. Small companies with specific domain expertise became the driving force for the niche market enabled by the Internet. The first evidence of the... 

Domain expertise - There are many complex factors to take into consideration when establishing severity and likelihood. These need to be examined by indi-vidnals with experience in the clinical domain. 

Consensus - Both the establishment of domain expertise and the elimination of bias can be facilitated by obtaining a consensus view of the severity and likelihood. The optimal balance is found when stakeholders across organisational and departmental boundaries are able to meet and collectively agree the evaluated clinical risk. 

Unfortunately, no tools exist for identifying hazards. It takes domain expertise and depends on subjective evaluation by those constructing the system. Chapter 13 in Safeware provides some common heuristics that may be helpful in the process. The good news is that identifying hazards is usually not a difficult process. The later steps in the hazard analysis process are where most of the mistakes and effort occurs. 

During the mathematical specification of the simulation model there is no need to bother about the correct parameter values to be used during the Monte Carlo simulation. Of course, this is addressed prior to running the simulations. In principle there are three kinds of sources for parameter values. The ideal source would consist of sufficient statistical data that has been gathered under the various contextual conditions for which the risk assessment has to be performed. In practice such ideal sources almost never exist. Instead one typically has to work with limited statistical data that has been gathered under different conditions. Fortunately there often are two complementary sources domain expertise and scientific expertise (on safety and human factors). In the context of Monte Carlo... 

The EEM taxonomy and domain expertise are then used to identify, based on the analyst s subjective judgment, any CTedible error modes for the task step in question. For each credible error identified, a description of the form that the error would take. 

Domain specific issues. Domain expertise and experience is very important and can often not be replaced by theoretical frameworks. A number of issues specific to the project or the domain were described, and it was considered important for them to be addressed explicitly. An important aspect of domain specificity is the visibihty of problems that other projects have encountered. This knowledge may allow developers to identify isstres early drrring the evaluation of the CertPack that may not otherwise be cortsidered. 

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