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Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools
- Source :
- Food and Chemical Toxicology, 142, Food and Chemical Toxicology 142 (2020)
- Publication Year :
- 2020
-
Abstract
- There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has the potential to provide a rapid and cost-effective method to identify compounds which may trigger steatosis. Although models exist to predict key molecular initiating events of steatosis such as nuclear receptor binding, we are aware of no models to predict the apical effect steatosis. In this study, we describe the development of a QSAR model to predict steatosis using freely available machine learning tools. It was built using a dataset of 207 pharmaceuticals and pesticides which were identified as steatotic or non-steatotic from existing data from in vivo human and animal studies. The best performing model developed using the linear discriminant analysis module in TANAGRA, based on four chemical descriptors, had an accuracy of 70 %, a sensitivity of 66% and a specificity of 74 %. The expansion of the steatosis dataset to other chemical types, to enable the development of further models, would be of benefit in the identification of compounds with a range of mechanisms of action contributing to steatosis.
- Subjects :
- Quantitative structure–activity relationship
Chemical descriptors
Steatosis
Novel Foods & Agrochains
Computer science
BU Toxicologie
QSAR model
Quantitative Structure-Activity Relationship
Toxicology
Machine learning
computer.software_genre
Novel Foods & Agroketens
Machine Learning
03 medical and health sciences
0404 agricultural biotechnology
Non-alcoholic Fatty Liver Disease
medicine
Humans
BU Toxicology, Novel Foods & Agrochains
Health risk
030304 developmental biology
0303 health sciences
business.industry
Fatty liver
BU Toxicology
04 agricultural and veterinary sciences
General Medicine
medicine.disease
Linear discriminant analysis
040401 food science
BU Toxicologie, Novel Foods & Agroketens
Environmental Pollutants
Artificial intelligence
business
computer
Algorithms
Food Science
Non-alcoholic fatty liver disease
Subjects
Details
- ISSN :
- 18736351 and 02786915
- Volume :
- 142
- Database :
- OpenAIRE
- Journal :
- Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
- Accession number :
- edsair.doi.dedup.....4e6d66ca514ed2b1800553c50c494823