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Drug-Related Side Effects and Adverse Reactions

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BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.

Medical & biological engineering & computing
Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and a...

Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology.

EBioMedicine
BACKGROUND: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality in health care. Understanding which drug targets are linked to ADRs can lead to the development of safer medicines.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

Scientific reports
During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alt...

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Molecular pharmaceutics
Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank da...

Borrowing external information to improve Bayesian confidence propagation neural network.

European journal of clinical pharmacology
PURPOSE: A Bayesian confidence propagation neural network (BCPNN) is a signal detection method used by the World Health Organization Uppsala Monitoring Centre to analyze spontaneous reporting system databases. We modify the BCPNN to increase its sens...

Quantitative Prediction of Hemolytic Toxicity for Small Molecules and Their Potential Hemolytic Fragments by Machine Learning and Recursive Fragmentation Methods.

Journal of chemical information and modeling
Hemolytic toxicity, as one of the key toxicity endpoints for small molecules, can cause lysis of the erythrocyte membrane and subsequent release of hemoglobin into blood plasma, leading to multiple acute and chronic adverse effects. Hence, it is nece...

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

Molecular pharmaceutics
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is ...

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

Journal of pharmacokinetics and pharmacodynamics
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity.

Molecular informatics
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is stil...

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

International journal of molecular sciences
Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is c...