Name
Abstract | ||
|---|---|---|
Motivation: Identification of rodent carcinogens is an important task in risk assessment of chemicals. SAR methods were proposed to reduce the number of animal experiments. Most of these methods ignore information about organ-specificity of tumorigenesis. Our study was aimed at the creation of classification models and a freely available online service for prediction of rodent carcinogens considering the species (rats, mice), sex and tissue-specificity from structural formula of compounds. Results: The data from Carcinogenic Potency Database for 1011 organic compounds evaluated on the standard two-year rodent carcinogenicity bioassay was used for the creation of training sets. Structure-activity relationships models for prediction of rodent organ-specific carcinogenicity were created by PASS software, which was based on Bayesian-like approach and Multilevel Neighborhoods of Atoms descriptors. The average prediction accuracy for training sets calculated by leave-one-out and 10-fold cross-validation was 79 and 78.2%, respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/btx678 | BIOINFORMATICS |
Field | DocType | Volume |
Computer science,Bioinformatics,Computational biology,Web service | Journal | 34 |
Issue | ISSN | Citations |
4 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A A Lagunin | 1 | 17 | 2.98 |
| Anastasia Rudik | 2 | 10 | 3.35 |
| Druzhilovsky Dmitry | 3 | 0 | 0.34 |
| Dmitry Filimonov | 4 | 53 | 7.45 |
| Vladimir Poroikov | 5 | 128 | 17.98 |