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How do you account for the uncertainty in Scoring Profiles?

Tuesday, 29 September 2009 21:21
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Ed Champness

The StarDrop Probabilistic Scoring algorithm accounts for the uncertainty in experimental results and in silico predictions. Although this effect is complicated to visualise for multi-dimensional data, it may be illustrated using the a simple threshold criteria for a single property. As a prediction or measurement gets closer to the threshold there becomes a significant probability that the ‘true’ value for the property actually exceeds the threshold line, as shown in the diagram. As this happens, the score begins to increase as the probability of success increases. Similarly, the maximum score is only achieved when the value itself is far enough above the threshold that there is a negligible chance, even when accounting for the errors in the measurement or prediction, that the true value will fail to exceed the threshold.

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