Automatic combination of multiple ranked retrieval systems
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
Probabilistic model for contextual retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Comparing Rank and Score Combination Methods for Data Fusion in Information Retrieval
Information Retrieval
Proceedings of the 16th international conference on World Wide Web
An Overview of BioCreative II.5
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes' global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score—two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II.5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II.5 IPT system.