Many existing bioinformatics predictors are based on machine learning technology. When applying these predictors in practical studies, their predictive performances should be well understood. Different performance mea...Many existing bioinformatics predictors are based on machine learning technology. When applying these predictors in practical studies, their predictive performances should be well understood. Different performance measures are applied in various studies as well as different evaluation methods. Even for the same performance measure, different terms, nomenclatures or notations may appear in different context. Results： We carried out a review on the most commonly used performance measures and the evaluation methods for bioinformatics predictors. Conclusions： It is important in bioinformatics to correctly understand and interpret the performance, as it is the key to rigorously compare performances of different predictors and to choose the right predictor.展开更多
基金This work was supported by the National Natural Science Foundation of China （NSFC 61005041）, the Specialized Research Natural Fund for the Doctoral Program of Higher Education （SRFDP 20100032120039）,Tianjin Natural Science Foundation （No. 12JCQNJC02300）, and China Postdoc- toral Science Foundation （Nos. 2012T50240 and 2013M530114）.
文摘Many existing bioinformatics predictors are based on machine learning technology. When applying these predictors in practical studies, their predictive performances should be well understood. Different performance measures are applied in various studies as well as different evaluation methods. Even for the same performance measure, different terms, nomenclatures or notations may appear in different context. Results： We carried out a review on the most commonly used performance measures and the evaluation methods for bioinformatics predictors. Conclusions： It is important in bioinformatics to correctly understand and interpret the performance, as it is the key to rigorously compare performances of different predictors and to choose the right predictor.
