DISCOVERY AND EXPLANATION OF DRUG-DRUG INTERACTIONS VIA TEXT MINING

By Bethany Percha, Yael Garten, Russ B. Altman. 2011

142 citations recorded by the source corpus.

Drug-drug interactions (DDIs) can occur when two drugs interact with the same gene product. Most available information about gene-drug relationships is contained within the scientific literature, but is dispersed over a large number of publications, with thousands of new publications added each month. In this setting, automated text mining is an attractive solution for identifying gene-drug relationships and aggregating them to predict novel DDIs. In previous work, we have shown that gene-drug interactions can be extracted from Medline abstracts with high fidelity - we extract not only the genes and drugs, but also the type of relationship expressed in individual sentences (e.g. metabolize, inhibit, activate and many others). We normalize these relationships and map them to a standardized ontology. In this work, we hypothesize that we can combine these normalized gene-drug relationships, drawn from a very broad and diverse literature, to infer DDIs. Using a training set of established DDIs, we have trained a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product. The classifier recognizes the combinations of relationships, drugs and genes that are most associated with the gold standard DDIs, correctly identifying 79.8% of assertions relating interacting drug pairs and 78.9% of assertions relating noninteracting drug pairs. Most significantly, because our text processing method captures the semantics of individual gene-drug relationships, we can construct mechanistic pharmacological explanations for the newly-proposed DDIs. We show how our classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “in order to enable the discovery of DDIs [60], [61] and ADEs [26], [62], [63], [64].”
Systematic prediction of drug combinations based on clinical side-effects (2014, methodology): “Several methods have been developed to predict drug-drug interactions (DDIs) based on text mining 36,37 , network modeling 38 , high-throughput screening 9 , and other data integrative approaches 13 .”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “Recent approaches have extended to utilizing semantic and syntactic analysis as well as statistical machine learning tools to mine targeted pharmacogenomics relations from scientific literature and clinical records [35–37].”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “[113] exploited the concept that two drugs can”
Data-Driven Prediction of Drug Effects and Interactions (2012, background): “A wide range of methods, from text mining ( 27 , 28 ) to network modeling ( 29 , 30 ), can detect, explain, and predict DDIs.”
Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “Existing DDI efforts have focused on explicit mentions of interactions in single sentences (Gurulingappa et al., 2012; Percha et al., 2012; Segura-Bedmar et al., 2013).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “[42] Stanford Enumerating dependency paths between a named en-”