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Automatic Generation of Negative Control Structures for Automated Structure Verification Systems
The generation of positive and negative controls is a fundamental part of good experimental design. Getting a positive outcome on a test performed over a subject known to give a positive result, reasures the scientist the test is working properly. As important, if not more, is to test over subjects known to give negative results. Getting a negative outcome when expected validates the test and increases the result’s confidence when applied to unknowns.
Automated Structure Verification (ASV) is no different than any other scientific test. Postive as well as negative controls should be frequently tested to optimize performance and to obtain a measure of robustness and confidence in the results.
In this poster I will show how to automatically generate relevant negative control structures for any type of NMR data. Furthermore, I will argue that ASV systems fall in the category of binary classifiers, and that their performance can be measured by a host of metrics, already in use in the fields of statistical classification and signal detection theory.
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