The exposome, a popular framework for studying exposures from chemical and nonchemical stressors across one’s lifespan, has been widely utilized to understand complex health trajectories and to link exposures to adverse outcome pathways. This method has raised the importance of preprocessing techniques in accurate and effective external exposomic data analysis. The focus on scalability is emphasized through the application of innovative combinatorial techniques and traditional statistical strategies.
The Public Health Exposome is used as an archetypical model for this process. The uniqueness of this approach lies in its comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. This systematic approach is crucial as it provides an essential first step in the application of modern computer and data science methods.
Advanced technologies are employed for data harmonization and to alleviate noise, which can hinder downstream interpretation. The task of reducing multicollinearity, a common problem that often arises from repeated measurements of similar events taken at different times and from different sources, is discussed. The importance of selecting key exposomic features is also highlighted, without which analytics may lose focus.
Unveiling the Power of Preprocessing in Exposome Science
Empirical results underscore the effectiveness of a well-planned preprocessing workflow. This is demonstrated by the presence of more concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The emerging field of exposome science is characterized by the need to analyze and interpret a complex array of highly heterogeneous spatial and temporal data.
This can present formidable challenges to even the most advanced analytical tools. Therefore, a systematic approach to preprocessing is an essential first step in the application of modern computer and data science methods. These insights contribute to the ongoing discussions in the field of exposome science and its potential role in understanding and improving human health.
Original Article DOI: 10.1289/EHP12901
Original title: Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis
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