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National Institute of Environmental Health Sciences

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Publication Detail

Title: Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments.

Authors: Odenkirk, Melanie T; Reif, David M; Baker, Erin S

Published In Anal Chem, (2021 Jun 08)

Abstract: The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

PubMed ID: 34029068 Exiting the NIEHS site

MeSH Terms: Artificial Intelligence*; Big Data; Data Analysis*

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