AIMC Topic: Toxicogenetics
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Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Apr 28, 2023
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the c...
A Novel Drug Repositioning Approach Based on Collaborative Metric Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Apr 6, 2021
Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that rec...
BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.
Medical & biological engineering & computing
Jul 24, 2020
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...
Chemical-induced disease relation extraction via attention-based distant supervision.
BMC bioinformatics
Jul 22, 2019
BACKGROUND: Automatically understanding chemical-disease relations (CDRs) is crucial in various areas of biomedical research and health care. Supervised machine learning provides a feasible solution to automatically extract relations between biomedic...
Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.
Assay and drug development technologies
May 31, 2019
in vivor p R.
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...
Gene2DisCo: Gene to disease using disease commonalities.
Artificial intelligence in medicine
Sep 4, 2017
OBJECTIVE: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease...
Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.
PloS one
May 12, 2016
Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Co...
Machine Learning on Toxicogenomic Data Reveals a Strong Association Between the Induction of Drug-Metabolizing Enzymes and Centrilobular Hepatocyte Hypertrophy in Rats.
International journal of molecular sciences
May 20, 2025
Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance and precise mechanism remain unknown, it is considered an adaptive response resulting from the induc...
Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.
Toxicological sciences : an official journal of the Society of Toxicology
Mar 28, 2022
Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep ...
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