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Two proteins are homologous if they have a common evolutionary origin, andthe binary classification problem is to identify proteins in a candidate setthat are homologous to a particular native protein. The feature (explanatory)variables available for classification are various measures of similarity ofproteins. There are multiple classification problems of this type for differentnative proteins and their respective candidate sets. Homologous proteins arerare in a single candidate set, giving a highly unbalanced two-class problem.The goal is to rank proteins in a candidate set according to the probability ofbeing homologous to the set's native protein. An ideal classifier will placeall the homologous proteins at the head of such a list. Our approach uses anensemble of models in a classifier and an ensemble of assessment metrics. For agiven metric a classifier combines models, each based on a subset of theavailable feature variables which we call phalanxes. The proposed ensemble ofphalanxes identifies strong and diverse subsets of feature variables. A secondphase of ensembling aggregates classifiers based on diverse evaluation metrics.The overall result is called an ensemble of phalanxes and metrics. It providerobustness against both close and distant homologues.

Ensembles of phalanxes across assessment metrics for robust ranking of homologous proteins