2013
DOI: 10.1016/j.tips.2013.01.006
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Informatics confronts drug–drug interactions

Abstract: Drug–drug interactions (DDIs) are an emerging threat to public health. Recent estimates indicate that DDIs cause nearly 74 000 emergency room visits and 195 000 hospitalizations each year in the USA. Current approaches to DDI discovery, which include Phase IV clinical trials and post-marketing surveillance, are insufficient for detecting many DDIs and do not alert the public to potentially dangerous DDIs before a drug enters the market. Recent work has applied state-of-the-art computational and statistical met… Show more

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Cited by 172 publications
(149 citation statements)
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“…The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets. Though these methods report areas under receiver operating characteristic (ROC) curves around 90% under cross-validation, their applicability in translational medicine and, thus, ability to reduce drug development costs has been controversial [2,4,5].…”
Section: Bodymentioning
confidence: 99%
See 1 more Smart Citation
“…The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets. Though these methods report areas under receiver operating characteristic (ROC) curves around 90% under cross-validation, their applicability in translational medicine and, thus, ability to reduce drug development costs has been controversial [2,4,5].…”
Section: Bodymentioning
confidence: 99%
“…The proposed models are typically benchmarked using cross-validation, in which the known drug-disease or drug-drug associations are split into training and test sets. Though these methods report areas under receiver operating characteristic (ROC) curves around 90% under cross-validation, their applicability in translational medicine and, thus, ability to reduce drug development costs has been controversial [2,4,5].In light of previous works highlighting the perils of cross-validation using paired data [6,7], we recently investigated the effect of using drug-wise disjoint cross-validation in predicting drug-disease pairs, where none of the drugs in the training set appeared in the test set [8]. We showed that the prediction accuracy of the classifier drops dramatically under such cross-validation setting, suggesting that the existing approaches are prone to over-fitting due to the inherent relationships in the data.…”
mentioning
confidence: 99%
“…Although manually curated databases that discuss DDIs exist [1], [4], most of the up-to-date information is still latent in unstructured text. Thus it is important to extract such interactions as they are presented as findings in research articles, warnings in drug labels, or observations in clinical notes [5]. The 2013 DDI extraction challenge [6], [7] introduced a new dataset and challenge to extract mentions of such interactions from free text narratives.…”
Section: Introductionmentioning
confidence: 99%
“…5 However, these methods rely on the availability of computable representations of DDI knowledge, which describe the general domain knowledge and can be understood and exploited by information systems. 6 During the last years, different research groups have attempted the formal representation of the DDI domain.…”
Section: ■ Introductionmentioning
confidence: 99%