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Tensor factorization toward precision medicine

Author(s)
Luo, Yuan; Wang, Fei; Szolovits, Peter
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
Precision medicine initiatives come amid the rapid growth in quantity and variety of biomedical data, which exceeds the capacity of matrix-oriented data representations and many current analysis algorithms. Tensor factorizations extend the matrix view to multiple modalities and support dimensionality reduction methods that identify latent groups of data for meaningful summarization of both features and instances. In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest that tensor applications could serve as an effective tool to enable frequent updating of medical knowledge based on the continually growing scientific and clinical evidence. We encourage extensive experimental studies to tackle challenges including design choice of factorizations, integrating temporality and algorithm scalability. Keywords: tensor factorization; precision medicine; biomedical data mining; multiple data modalities
Date issued
2017-05
URI
https://hdl.handle.net/1721.1/121585
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Luo, Yuan et al. "Tensor factorization toward precision medicine." Briefings in Bioinformatics 18, 3 (May 2017): 511-514 © 2016 The Authors
Version: Author's final manuscript
ISSN
1467-5463
1477-4054

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