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Emerging Approaches to Integrate Human Clinical and High Throughput In Vitro Data

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Integrating large quantities of data from multiple, disparate sources can create new opportunities to understand complex environmental health questions. However, combining new types and larger quantities of data to inform a specific decision presents many new challenges. For example, investigators must develop methods to reliably integrate data from designed experiments with data re-purposed from other uses, such as electronic health records, geo-spatial data sets, and crowd-based sources.

About Past Events

Description

An ad hoc committee will organize and convene a public workshop to explore the integration of phenotypic data (e.g. epidemiology and clinical data) with mechanistic data gathered from molecular and in vitro studies as a novel approach for informing environmental decisions. The workshop will explore various applications for these "big data" analysis and the complementary research areas that are data rich, such as toxicogenomics and gene expression data sets, that have not yet been adequately assessed. The workshop will also examine the application of novel and advanced data science techniques for environmental health research. Among the topics that may be discussed are new methods for creating, processing and interrogating large datasets; novel methods for examining the safety of novel therapeutics (in vitro and in silico); and the convergence of data science and molecular biology. In addition, the committee will consider discussion topics such as:

  • The feasibility of integrating data from multiple sources such as electronic health records and genome wide association studies to study environmental determinants of health outcomes
  • Advances in computational toxicology with the rise of big data, novel data integration tools, and increased access to powerful computing capabilities that can be used to predict chemical interactions and be integrated into risk assessments
  • Applications of "big data" to different environmental health decision-making contexts, such as whether and how to move big data tools and methods "discovery" to real-world applications
  • The ability of machine learning techniques and multivariate methods to be applied to environmental health datasets
  • The use of data visualization techniques for environmental health research and to facilitate end user understanding of research conclusions being drawn from the integration of large data sets.

This workshop will build upon discussions of previous work of the Standing Committee on Emerging Science for Environmental Health Decisions, including the 2013 meeting, Integrating Environmental Health Data to Advance Discovery, and the 2009 meeting, Computational Toxicology: From Data to Analyses to Applications.

The workshop will result in a workshop proceedings, written by a designated rapporteur in accordance with institutional guidelines.

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