Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests.

@article{Zhu2016ChemicalAI,
  title={Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests.},
  author={X-W Zhu and Y-J Xin and Q-H Chen},
  journal={SAR and QSAR in environmental research},
  year={2016},
  volume={27 7},
  pages={559-72}
}
In this study, recursive random forests were used to build classification models for mouse liver toxicity. The mouse liver toxicity endpoint (67 toxic and 166 non-toxic) was a composition of four in vivo chronic systemic and carcinogenic toxicity endpoints (non-proliferative, neoplastic, proliferative and gross pathology). A multiple under-sampling approach and a shifted classification threshold of 0.288 (non-toxic < 0.288 and toxic ≥ 0.288) were used to cope with the unbalanced data. Our study… CONTINUE READING