  Biological data including gene expression data are generally high-dimensional
and require efficient, generalizable, and scalable machine-learning methods to
discover their complex nonlinear patterns. The recent advances in machine
learning can be attributed to deep neural networks (DNNs), which excel in
various tasks in terms of computer vision and natural language processing.
However, standard DNNs are not appropriate for high-dimensional datasets
generated in biology because they have many parameters, which in turn require
many samples. In this paper, we propose a DNN-based, nonlinear feature
selection method, called the feature selection network (FsNet), for
high-dimensional and small number of sample data. Specifically, FsNet comprises
a selection layer that selects features and a reconstruction layer that
stabilizes the training. Because a large number of parameters in the selection
and reconstruction layers can easily result in overfitting under a limited
number of samples, we use two tiny networks to predict the large, virtual
weight matrices of the selection and reconstruction layers. Experimental
results on several real-world, high-dimensional biological datasets demonstrate
the efficacy of the proposed method.
