# Difference between revisions of "LogAUC"

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==What is LogAUC?== | ==What is LogAUC?== | ||

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− | When we look at virtual screening performance, we plot an ROC curve (or enrichment curve) with a base 10 semilog x-axis, because this has the advantage of focusing the graph on "early enrichment", where molecules are most likely to be selected for further testing. If we had instead plotted the curve with the usual linear x-axis, then the area under the curve (AUC) is a well-regarded metric to summarize the overall performance of a virtual screening campaign as a single number. | + | When we look at virtual screening performance, we plot an ROC curve (or enrichment curve) with a base 10 semilog x-axis, because this has the advantage of focusing the graph on "early enrichment", where molecules are most likely to be selected for further testing. If we had instead plotted the curve with the usual linear x-axis, then the area under the curve (AUC) is a well-regarded metric to summarize the overall performance of a virtual screening campaign as a single number<sup>1</sup>. While ROC AUC can be formulated alternate ways, it can be |

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+ | <math>LogAUC_\lambda=\frac{\displaystyle \sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}</math> | ||

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+ | ==References== |

## Revision as of 00:00, 13 January 2010

## What is LogAUC?

LogAUC is a metric to evaluate virtual screening performance that has some nice characteristics. It is intuitive to use

## Motivation

When we look at virtual screening performance, we plot an ROC curve (or enrichment curve) with a base 10 semilog x-axis, because this has the advantage of focusing the graph on "early enrichment", where molecules are most likely to be selected for further testing. If we had instead plotted the curve with the usual linear x-axis, then the area under the curve (AUC) is a well-regarded metric to summarize the overall performance of a virtual screening campaign as a single number^{1}. While ROC AUC can be formulated alternate ways, it can be

<math>LogAUC_\lambda=\frac{\displaystyle \sum_{i}^{where~x_i\ge\lambda} (\log_{10} x_{i+1} - \log_{10} x_i)(\frac{y_{i+1}+y_i}{2})}{\log_{10}\frac{1}{\lambda}}</math>