A dynamic programming re-ranking approach to enhance PPI interactor extraction

Experimentally verified protein-protein interactions (PPIs) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by employing text-mining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers, also referred to as the interactor normalization task (INT). Our previous INT system won first place in the BioCreAtIvE II.5 INT challenge by exploiting the different characteristics of individual paper sections to guide gene normalization (GN) and using a support-vector-machine (SVM)-based ranking procedure. The best AUC achieved by our original system was 0.435 in the BioCreAtIvE II.5 INT offline challenge. After employing the proposed re-ranking algorithm, we have been able to improve our system's AUC to 0.447. In this paper, we present a new relational re-ranking algorithm that considers the associations among identifiers to further improve INT ranking results.