AIMC Topic: Toxicology
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The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.
Expert review of gastroenterology & hepatology
Oct 9, 2017
Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can b...
ADMETNet: The knowledge base of pharmacokinetics and toxicology network.
Journal of genetics and genomics = Yi chuan xue bao
May 3, 2017
Surface enhanced Raman spectroscopy (SERS) as a method for the toxicological analysis of synthetic cannabinoids.
Talanta
Nov 12, 2016
Synthetic cannabinoids (K2, spice) present problems in forensic investigations because standard presumptive methods, such as immunoassays, are insufficiently specific for the wide range of potential target compounds. This issue can lead to problems w...
An in silico expert system for the identification of eye irritants.
SAR and QSAR in environmental research
May 13, 2015
This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient c...
Comparing answers of artificial intelligence systems and clinical toxicologists to questions about poisoning: Can their answers be distinguished?
Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
Oct 1, 2024
OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers can identify the source of the answers. To evaluate and compare text quality and level of knowledge f...
E-validation - Unleashing AI for validation.
ALTEX
Jan 1, 2024
The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article propo...
A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.
Toxicological sciences : an official journal of the Society of Toxicology
May 1, 2019
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidim...
Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection.
The Journal of toxicological sciences
Jan 1, 2019
For the safety assessment of pharmaceuticals, initial data management requires accurate toxicological data acquisition, which is based on regulatory safety studies according to guidelines, and computational systems have been developed under the appli...
Machine Learning Methods in Computational Toxicology.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2018
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of ...
Chemical-induced disease relation extraction via convolutional neural network.
Database : the journal of biological databases and curation
Jan 1, 2017
UNLABELLED: This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentenc...
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