AIMC Topic: Drug Interactions

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Multichannel Convolutional Neural Network for Biological Relation Extraction.

BioMed research international Dec 7, 2016
The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are suscep...
Machine Learning Algorithms Biomedical Research Data Mining Protein Interaction Mapping Support Vector Machine Drug Interactions Neural Networks, Computer
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Identifying Drug-Drug Interactions by Data Mining: A Pilot Study of Warfarin-Associated Drug Interactions.

Circulation. Cardiovascular quality and outcomes Nov 8, 2016
BACKGROUND: Knowledge about drug-drug interactions commonly arises from preclinical trials, from adverse drug reports, or based on knowledge of mechanisms of action. Our aim was to investigate whether drug-drug interactions were discoverable without ...
Aged, 80 and over Registries Blood Coagulation Drug Interactions Male Warfarin Polypharmacy Atrial Fibrillation Data Mining Pilot Projects International Normalized Ratio Female Drug Prescriptions Logistic Models Administrative Claims, Healthcare Predictive Value of Tests Risk Factors Anticoagulants Aged Humans Machine Learning Retrospective Studies
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Introducing the Big Knowledge to Use (BK2U) challenge.

Annals of the New York Academy of Sciences Oct 17, 2016
The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK),...
Pharmaceutical Preparations Drug Interactions Translational Research, Biomedical Image Processing, Computer-Assisted Computational Biology Systematized Nomenclature of Medicine Humans Models, Neurological Terminology as Topic Animals Knowledge Bases Intracranial Hemorrhages Data Mining Decision Making, Computer-Assisted Image Interpretation, Computer-Assisted
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Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Journal of chemical information and modeling Aug 26, 2016
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activity. Accordingly, ACs are of high interest for the exploration of structure-activity relationships (SARs). ACs reveal small chemical modifications that ...
Drug Discovery Support Vector Machine Drug Interactions Quantitative Structure-Activity Relationship Informatics
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Persisting Hypocalcemia After Surgical Parathyroidectomy: The Differential Effectiveness of Calcium Citrate Versus Calcium Carbonate With Acid Suppression.

The American journal of the medical sciences Apr 22, 2016
The effectiveness of oral calcium (Ca) may be contingent on a patient׳s factors beyond compliance, such as proton-pump inhibitor use and the choice of calcium supplements. A 32-year-old Hispanic male with end-stage renal disease on peritoneal dialysi...
Calcium Carbonate Calcium Citrate Parathyroidectomy Hypocalcemia Proton Pump Inhibitors Renal Dialysis Kidney Failure, Chronic Drug Interactions Adult Humans Male
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Extracting drug-enzyme relation from literature as evidence for drug drug interaction.

Journal of biomedical semantics Mar 7, 2016
BACKGROUND: Information about drug-drug interactions (DDIs) is crucial for computational applications such as pharmacovigilance and drug repurposing. However, existing sources of DDIs have the problems of low coverage, low accuracy and low agreement....
Drug Interactions Data Mining Biological Ontologies Machine Learning Protein Binding Pharmaceutical Preparations Enzymes
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Drug-Drug Interaction Extraction via Convolutional Neural Networks.

Computational and mathematical methods in medicine Jan 31, 2016
Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a lar...
Machine Learning Algorithms Support Vector Machine Models, Statistical Humans Neural Networks, Computer Data Mining Semantics Pattern Recognition, Automated Regression Analysis Natural Language Processing Drug Interactions
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A randomized-controlled clinical trial investigating the effect of omega-3 fatty acids and vitamin E co-supplementation on markers of insulin metabolism and lipid profiles in gestational diabetes.

Journal of clinical lipidology Dec 29, 2015
BACKGROUND: Limited data are available that evaluated the effects of combined omega-3 fatty acids and vitamin E supplementation on glucose homeostasis parameters and lipid concentrations in gestational diabetes (GDM).
Humans Female Adult Biomarkers Pregnancy Fatty Acids, Omega-3 Homeostasis Vitamin E Lipids Drug Interactions Blood Glucose Diabetes, Gestational Insulin Dietary Supplements
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Drug-drug Interaction Discovery Using Abstraction Networks for "National Drug File - Reference Terminology" Chemical Ingredients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Nov 5, 2015
The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology. NDF-RT provides important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects...
Vocabulary, Controlled Drug Interactions Knowledge Bases Humans Databases, Factual
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Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug-drug interaction extraction and classification.

Journal of biomedical informatics Sep 30, 2015
Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. An essential aspect in PV is to ...
Data Mining Pharmacovigilance Machine Learning Drug Interactions
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