Tensor factorization toward precision medicine
Name
tensor_biomedicine.pdf
Description
Accepted version
Size
409.94 KB
Format
Adobe PDF
Checksum (MD5)
adee12a6a2727af125faec358f7c2172
Author(s)
Luo, Yuan
Wang, Fei
Szolovits, Peter
Date Issued
May 2017
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
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
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bib/bbw026
