Translational informatics of population health: How large biomolecular and clinical datasets unite
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Final Published Version
Affiliation
Univ Arizona, Ctr Biomed Informat & Biostat, Dept Med, BIO5 Inst,Canc CtrUniv Arizona, Coll Agr & Life Sci
Issue Date
2019
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WORLD SCIENTIFIC PUBL CO PTE LTDCitation
Lussier, Y. A., Butte, A. J., Li, H., Chen, R., & Moore, J. H. (2019, January). Translational informatics of population health: How large biomolecular and clinical datasets unite. In PSB (p. 455).Rights
© 2018 The Authors. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
This paper summarizes the workshop content on how the integration of large biomolecular and clinical datasets can enhance the field of population health via translational informatics. Large volumes of data present diverse challenges for existing informatics technology, in terms of computational efficiency, modeling effectiveness, statistical computing, discovery algorithms, and heterogeneous data integration. While accumulating large 'omics measurements on subjects linked with their electronic record remains a challenge, this workshop focuses on non-trivial linkages between large clinical and biomolecular datasets. For example, exposures and clinical datasets can relate through zip codes, while comorbidities and shared molecular mechanisms can relate diseases. Workshop presenters will discuss various methods developed in their respective labs/organizations to overcome the difficulties of combining together such large complex datasets and knowledge to enable the translation to clinical practice for improving health outcomes.Note
Open access journalISSN
2335-6936Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1142/9789813279827_0043
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Except where otherwise noted, this item's license is described as © 2018 The Authors. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License.

