TensorGxG: A sparse and low-rank screening based on the combination of a low-rank interaction model and the Lasso screening (Matlab).

TitleTensorGxG: A sparse and low-rank screening based on the combination of a low-rank interaction model and the Lasso screening (Matlab).
Publication TypeSoftware
Year of Publication2015
AuthorsHung, Hung, Yu-Ting Lin, Penweng Chen, Chen-Chien Wang, Su-Yun Huang, and Jung-Ying Tzeng
Abstract

A sparse and low-rank (SLR) screening based on the combination of a low-rank interaction model and the Lasso screening. SLR models the interaction effects using a low-rank matrix to achieve parsimonious parametrization. The low-rank model increases the efficiency of statistical inference and, hence, SLR screening is able to more accurately detect gene–gene interactions than conventional methods.

Original PublicationTensorGxG: A sparse and low-rank screening based on the combination of a low-rank interaction model and the Lasso screening (Matlab).
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