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STATISTICAL RELATIONAL LEARNING GROUP @ UMD



 

A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction

Dhanya Sridhar, Shobeir Fakhraei, Lise Getoor
Bioinformatics - 2016
Download the publication : sridhar-bioinformatics_2016.pdf [2Mo]  
Motivation: As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions can guide in-vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their associated targets, such methods must effectively fuse multiple sources of information and perform inference over the network of drugs. Results: We propose a probabilistic approach for jointly inferring unknown drug-drug interactions from a network of multiple drug-based similarities and known interactions. We use the highly scalable and easily extensible probabilistic programming framework Probabilistic Soft Logic. We compare against two methods including a state-of-the-art drug-drug interaction prediction system across three experiments and show best performing improvements of more than 50% in AUPR over both baselines. We find five novel interactions validated by external sources among the top-ranked predictions of our model.

BibTex references

@Article{sridhar:bioinformatics16,
  author       = "Sridhar, Dhanya and Fakhraei, Shobeir and Getoor, Lise",
  title        = "A Probabilistic Approach for Collective Similarity-based Drug-Drug Interaction Prediction",
  journal      = "Bioinformatics",
  year         = "2016",
}

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