NDLI: The systems toxicology challenge: how to leverage omics data to predict mechanisms of toxicity?

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The systems toxicology challenge: how to leverage omics data to predict mechanisms of toxicity?

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The systems toxicology challenge: how to leverage omics data to predict mechanisms of toxicity?

Content Provider	ACM Digital Library
Author	Hoeng, Julia Sewer, Alain Peitsch, Manuel C. Poussin, Carine Titz, Bjoern Ivanov, Nikolai V. Boué, Stéphanie Martin, Florian
Abstract	Risk assessment in the context of 21st century toxicology relies on the elucidation and understanding of mechanisms of toxicity. For that purpose, datasets generated by high-throughput technologies (e.g., high-throughput/content screening) combined with various omics data types are now generated in vitro to test large and diverse set of chemicals (e.g. ToxCast). The development of relevant computational approaches for the analysis and integration of these big data remains challenging and requires qualitative and quantitative evaluation. The current scope of sbv IMPROVER (Industrial Methodology for Process Verification in Research; http://sbvimprover.com) is the verification of methods and concepts in systems biology research via challenges opened to the scientific community. Previous challenges brought new insights on methods and their associated results that address questions about diagnostic signatures, the translatability of biological responses/processes across species, and the relevance of biological causal network models. A new sbv IMPROVER challenge will be introduced aiming at evaluating (i) methodologies for the identification of specific biomarkers of exposure and (ii) the predictability by omics data of toxicity mechanisms when cells/tissues in vitro or whole organisms are exposed to individual chemical molecules or mixtures. Participants will be provided with high quality data sets to develop predictive models/classifiers. For this challenge, the integration of a priori biological knowledge in the development of computational approaches may be required to enable biological interpretability/understanding of the predictions. The results and post-challenge analyses will be shared with the scientific community, and will open new avenues in the field of systems toxicology.
Starting Page	487
Ending Page	487
Page Count	1
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2811421
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Systems biology Omics Sbv improver Systems toxicology
Content Type	Text
Resource Type	Article

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