Molecular Systems Biology Peer Review Process File Indi: a Computational Framework for Inferring Drug Interactions and Their Associated Recommendations Transaction Report

By Assaf Gottlieb, Gideon Y Stein, Y. Oron, Eytan Ruppin, Roded Sharan

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Citations in the corpus

Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. (2014, methodology): “The elements of the tree generated by ID3 and C4.5 DT include leaf and decision nodes.”
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data (2017, background): “Gottlieb et al. [18] built prediction models by considering seven kinds of drug-drug similarities.”
Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects (2015, methodology): “While Gottlieb et al. 15 used SEs as one of the sources to build predictive models, they did not consider any off-label SEs extracted from FDA Adverse Event Reporting System.”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “contrast, similarity-based approaches assume that similar drugs have similar interaction patterns [296, 297, 287, 252, 33, 289, 298].”
Deep learning improves prediction of drug–drug and drug–food interactions (2018, background): “Also, many of the computational methods often require a large volume of detailed drug information such as drug targets, inter-acting drugs, and side effects, which are often unavailable, as input to predict DDIs (7, 9 – 14).”
Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge (2017, background): “Based on the similar idea, INferring drug interactions (INDI), was developed to predict CYP-related and PD-related DDI using drug chemical similarities, side effects similarities, ATC (Anatomical Therapeutic Chemical classification system) similarities, target sequence similarities, protein–protein ”
Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics (2014, background): “Drug similarity analytics has been widely used in drug repositioning 7-9 , drug side-effects prediction 10 , drug-target interactions prediction 11 , and drug-drug interactions prediction 12, 13 applications.”