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

TL;DR

This paper introduces an ensemble approach combining models based on feature subsets and multiple assessment metrics to improve the ranking of homologous proteins, enhancing robustness in highly unbalanced classification tasks.

Contribution

The novel ensemble of phalanxes and metrics effectively identifies diverse feature subsets and evaluation criteria, improving protein homology ranking accuracy.

Findings

Enhanced robustness against close and distant homologues.

Effective handling of highly unbalanced classification.

Improved ranking performance of homologous proteins.

Abstract

Two proteins are homologous if they have a common evolutionary origin, and the binary classification problem is to identify proteins in a candidate set that are homologous to a particular native protein. The feature (explanatory) variables available for classification are various measures of similarity of proteins. There are multiple classification problems of this type for different native proteins and their respective candidate sets. Homologous proteins are rare 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 of being homologous to the set's native protein. An ideal classifier will place all the homologous proteins at the head of such a list. Our approach uses an ensemble of models in a classifier and an ensemble of assessment metrics. For a given metric a classifier combines models, each based on a subset of the available feature variables which we call phalanxes. The proposed ensemble of phalanxes identifies strong and diverse subsets of feature variables. A second phase of ensembling aggregates classifiers based on diverse evaluation metrics. The overall result is called an ensemble of phalanxes and metrics. It provide robustness against both close and distant homologues.