Exploring classification strategies with the CoEPrA 2006 contest.

Bioinformatics
Ozgur Demir-KavukErnst-Walter Knapp

Abstract

In silico methods to classify compounds as potential drugs that bind to a specific target become increasingly important for drug design. To build classification devices training sets of drugs with known activities are needed. For many such classification problems, not only qualitative but also quantitative information of a specific property (e.g. binding affinity) is available. The latter can be used to build a regression scheme to predict this property for new compounds. Predicting a compound property explicitly is generally more difficult than classifying that the property lies below or above a given threshold value. Hence, an indirect classification that is based on regression may lead to poorer results than a direct classification scheme. In fact, initially researchers are only interested to classify compounds as potential drugs. The activities of these compounds are subsequently measured in wet lab. We propose a novel approach that uses available quantitative information directly for classification rather than first using a regression scheme. It uses a new type of loss function called weighted biased regression. Application of this method to four widely studied datasets of the CoEPrA contest (Comparative Evaluation of Pred...Continue Reading

References

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