  A network lasso enables us to construct a model for each sample, which is
known as multi-task learning. Existing methods for multi-task learning cannot
be applied to compositional data due to their intrinsic properties. In this
paper, we propose a multi-task learning method for compositional data using a
sparse network lasso. We focus on a symmetric form of the log-contrast model,
which is a regression model with compositional covariates. The effectiveness of
the proposed method is shown through simulation studies and application to gut
microbiome data.
