Large p small n problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100.

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[1] Y-h. Taguchi, "Metascape results for Prostate cancer multiomics data", IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/rdmb-jm40. Accessed: Feb. 25, 2024.
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doi = {10.21227/rdmb-jm40},
url = {http://dx.doi.org/10.21227/rdmb-jm40},
author = {Y-h. Taguchi },
publisher = {IEEE Dataport},
title = {Metascape results for Prostate cancer multiomics data},
year = {2020} }
TY - DATA
T1 - Metascape results for Prostate cancer multiomics data
AU - Y-h. Taguchi
PY - 2020
PB - IEEE Dataport
UR - 10.21227/rdmb-jm40
ER -
Y-h. Taguchi. (2020). Metascape results for Prostate cancer multiomics data. IEEE Dataport. http://dx.doi.org/10.21227/rdmb-jm40
Y-h. Taguchi, 2020. Metascape results for Prostate cancer multiomics data. Available at: http://dx.doi.org/10.21227/rdmb-jm40.
Y-h. Taguchi. (2020). "Metascape results for Prostate cancer multiomics data." Web.
1. Y-h. Taguchi. Metascape results for Prostate cancer multiomics data [Internet]. IEEE Dataport; 2020. Available from : http://dx.doi.org/10.21227/rdmb-jm40
Y-h. Taguchi. "Metascape results for Prostate cancer multiomics data." doi: 10.21227/rdmb-jm40