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High-throughput literature mining to support read-across predictions of toxicity
Citation:
Baker, N., T. Knudsen, K. Crofton, AND G. Patlewicz. High-throughput literature mining to support read-across predictions of toxicity. In Proceedings, American Society for Cellular and Computational Toxicology, Durham, NC, September 29 - 30, 2016. Society ALTEX Edition, Kuesnacht, Switzerland, 1, (2017).
Impact/Purpose:
Meeting extended abstract for proceedings.
Description:
Building scientific confidence in the development and evaluation of read-across remains an ongoing challenge. Approaches include establishing systematic frameworks to identify sources of uncertainty and ways to address them. One source of uncertainty is related to characterizing biological similarity (Shah, Liu et al. 2016). Many research efforts are underway such as structuring mechanistic data in adverse outcome pathways and investigating the utility of high throughput screening (HTS) and high content screening (HCS) data (Kavlock, Chandler et al. 2012). A largely untapped resource for read-across is the biomedical literature. This information has the potential to support read-across by facilitating the identification of valid source analogues with similar biological and toxicological profiles as well as providing the mechanistic understanding for any prediction made.