In silico toxicology: comprehensive benchmarking of multi-label classification methods applied to chemical toxicity data.

ABSTRACT: One goal of toxicity testing, among others, is identifying harmful effects of chemicals. Given the high demand for toxicity tests, it is necessary to conduct these tests for multiple toxicity endpoints for the same compound. Current computational toxicology methods aim at developing models mainly to predict a single toxicity endpoint. When chemicals cause several toxicity effects, one model is generated to predict toxicity for each endpoint, which can be labor and computationally intensive when the number of toxicity endpoints is large. Additionally, this approach does not take into consideration possible correlation between the endpoints. Therefore, there has been a recent shift in computational toxicity studies toward generating predictive models able to predict several toxicity endpoint

SUBMITTER: Raies AB 

PROVIDER: S-EPMC5947741 | biostudies-literature | 2018 May-Jun

REPOSITORIES: biostudies-literature