Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 28
- Type
- D - Journal article
- DOI
-
10.1007/s00500-015-1925-9
- Title of journal
- Soft Computing
- Article number
- -
- First page
- 2967
- Volume
- 20
- Issue
- 8
- ISSN
- 1432-7643
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://link.springer.com/article/10.1007%2Fs00500-015-1925-9
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. The proposed methodology is widely applicable within the scientific domain and beyond for comprehensively building classification models to compare functional relationships between two variables.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
