AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Chemical and Drug Induced Liver Injury

Showing 41 to 50 of 64 articles

Clear Filters

The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications.

Briefings in bioinformatics
In the previous study, we developed the generalized drug-induced liver injury (DILI) prediction model-ResNet18DNN to predict DILI based on multi-source combined DILI dataset and achieved better performance than that of previously published described ...

Machine Learning from Omics Data.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through...

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...

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...

Predictability of drug-induced liver injury by machine learning.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucia...

Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.

Molecules (Basel, Switzerland)
Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming and studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are approaches, which present a cost-efficient...

Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints.

BioMed research international
Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even with...