Computational toxicology demands accessible and interoperable sources of data from new approach methods, such as high-throughput screening for bioactivity, as well as reference data from traditional approaches and exposure information. The goals of constructing these data resources include standardization of the preliminary analysis of raw data from multi-dimensional high-throughput screening assays; sharing of information that may be useful for understanding chemical risk, and benchmarking the performance of new approach methods to currently understood methods that may involve the use of whole animals. Data pipelining and construction of databases as toxicology resources are often overlooked but integral components of how we understand and use these data for predictive toxicology applications. In this Research Topic collection, we intend to focus on updates and new data analysis pipelines, construction of database resources, and other computational tasks aimed at making data accessible and interpretable for toxicology.
The goal of this Research Topic is to highlight the considerations built in to data analysis pipelines and the construction of datasets and databases. For computational toxicology approaches to be applied to relevant applications, the details of these resources must be transparent and understood by the user community. This Research Topic will provide a home to the technical construction of these resources that inform hypothesis-driven and applied toxicology research.
Data analysis pipelines include managing raw data, preliminary analysis, curve-fitting, and other approaches to datasets comprised of many endpoints. Construction of databases and other data resources for benchmarking new approach method performance. Methods applied to data to reduce or manage complexity in the data and otherwise make these data available for downstream analyses.
--
Different Article Types can be submitted to the Research Topic including Original Research, Review, Mini-Review, Brief Research Report, Perspective articles, among others. You can find detailed information
here.
--
Computational toxicology demands accessible and interoperable sources of data from new approach methods, such as high-throughput screening for bioactivity, as well as reference data from traditional approaches and exposure information. The goals of constructing these data resources include standardization of the preliminary analysis of raw data from multi-dimensional high-throughput screening assays; sharing of information that may be useful for understanding chemical risk, and benchmarking the performance of new approach methods to currently understood methods that may involve the use of whole animals. Data pipelining and construction of databases as toxicology resources are often overlooked but integral components of how we understand and use these data for predictive toxicology applications. In this Research Topic collection, we intend to focus on updates and new data analysis pipelines, construction of database resources, and other computational tasks aimed at making data accessible and interpretable for toxicology.
The goal of this Research Topic is to highlight the considerations built in to data analysis pipelines and the construction of datasets and databases. For computational toxicology approaches to be applied to relevant applications, the details of these resources must be transparent and understood by the user community. This Research Topic will provide a home to the technical construction of these resources that inform hypothesis-driven and applied toxicology research.
Data analysis pipelines include managing raw data, preliminary analysis, curve-fitting, and other approaches to datasets comprised of many endpoints. Construction of databases and other data resources for benchmarking new approach method performance. Methods applied to data to reduce or manage complexity in the data and otherwise make these data available for downstream analyses.
--
Different Article Types can be submitted to the Research Topic including Original Research, Review, Mini-Review, Brief Research Report, Perspective articles, among others. You can find detailed information
here.
--
