Experimental Data Generation & Processing
Session Chair: Ignacio Gonzalez Suarez (Philip Morris International)
Thursday, 1 October 2015
from 13.35 to 15:15
|Ignacio Gonzalez Suarez, PMI||HCS data sharing: Overcoming the Challenges - How, What & Why|
|Keith Houck, US EPA||The US EPA's ToxCast Project: A Public Resource for Environmental Chemical High-Throughput Testing Data|
|Yanli Wang, NCBI||The PubChem's practice for hosting high throughput screening (HTS) data|
|Barry Hardy, Douglas Connect||A ToxBank Integrated Data Analysis of SEURAT-1 Reference Compounds|
Over the past decade, toxicologists have used high-throughput screening (HTS) and high content imaging (HCI) to identify and prioritize chemicals that are potentially harmful for living organisms. Furthermore, the incorporation of computational tools to laboratory data analysis allowed for the generation of predictive models and the identification of a number of adverse outcome pathways. Still, predictive toxicology requires not only multidisciplinary teams to analyze high-throughput quantitative data, but also verification of the conclusions from such analyses in order to ensure accuracy and reproducibility.
Classical peer review mainly evaluates suitability of data collection protocols, accuracy of inferences and ideas, innovation, the logic of the argument and the consistency of the material being reviewed. However, it is often unfeasible or difficult for reviewers to assess the quality of data itself, or the performance of the analytical methodologies described in the manuscript, as the data is not readily available/ accessible.
In order to address these challenges, the scientific community would require a repository specifically designed for the deposition of HTS / HCS data and containing all the necessary information, so that other groups can reproduce the results of the study. Moreover, the data should be annotated in a standardized format, so that multiple studies can be compared simultaneously.
The aim of the session is to try to put forward a series of recommendations for the annotation and sharing of HCS / HTS-derived data catering for the different needs of industry and academic researchers as well as regulators.
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