J Hazard Mater 2021 Sep 11;423(Pt B):127141. Epub 2021 Sep 11.
Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA; School of Civil and Environmental Engineering, Cornell University, 263 Hollister Hall, Ithaca, NY 14853, USA. Electronic address:
One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy. Read More