Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles

Background: 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 facilitated by employing textmining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers (interactor normalization task or INT) and then to return a list of interaction pairs for each article (interaction pair task or IPT). These two tasks are evaluated in terms of the area under curve of the interpolated precision/recall (AUC iP/R) score because the order of identifiers in the output list is important for ease of curation. Results: Our INT system developed for the BioCreAtIvE II.5 INT challenge achieved a promising AUC iP/R of 43.5% by using a support vector machine (SVM)-based ranking procedure. Using our new re-ranking algorithm, we have

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Richard Tzong-Han Tsai, Po-Ting Lai
• Keywords: Artificial Intelligence, BMCBI 2011, Interaction Pair, Interaction Pair Task, Interactor Normalization Task