title: Balanced ROC analysis (BAROC) protocol for the evaluation of protein similarities.
abstract: Identification of problematic protein classes (domain types, protein families) that are difficult to predict from sequence is a key issue in genome annotation. ROC (Receiver Operating Characteristic) analysis is routinely used for the evaluation of protein similarities, however its results - the area under curve (AUC) values - are differentially biased for the various protein classes that are highly different in size. We show the bias can be compensated for by adjusting the length of the top list in a class-dependent fashion, so that the number of negatives within the top list will be equal to (or proportional with) the size of the positive class. Using this balanced protocol the problematic classes can be identified by their AUC values, or by a scatter diagram in which the AUC values are plotted against positive/negative ratio of the top list. The use of likelihood-ratio scoring (Kaján et al, Bioinformatics,22, 2865-2869, 2007) the bias caused by class imbalance can be further decreased.
authors: Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1., H-6720 Szeged, Hungary. busarobi@inf.u-szeged.hu
authors: Róbert
authors: R
authors: Busa-Fekete
authors: Attila
authors: A
authors: Kertész-Farkas
authors: András
authors: A
authors: Kocsor
authors: Sándor
authors: S
authors: Pongor
mesh_terms: D000465:Algorithms; D011506:Proteins / Q000032:analysis*; D012372:ROC Curve
chemical_list: D011506:Proteins