Host: The Japanese Society of Toxicology
Prediction of toxicity of chemical substances in human with alternative testing methods is essential in cosmetic industries. The combined approach of the transcriptomic database in undifferentiated cells and machine learning technology are expected to be a useful tool that can comprehensively evaluate the reactivity of chemical substances in human. (Yamane et al. Nucleic Acids Res. 2016) However, the direct use of human ES cells to assess the safety of chemicals in humans also presents ethical challenges. We selected the representable human iPS cells among 28 cell lines that show a highly similar response to ES cells in databases. Here we report a novel prediction system of toxicities (eg. liver etc) using gene expression network in human iPS cells and machine learning.
