Genes are fundamental for analyzing biological systems and many recent works
proposed to utilize gene expression for various biological tasks by deep
learning models. Despite their promising performance, it is hard for deep
neural networks to provide biological insights for humans due to their
black-box nature. Recently, some works integrated biological knowledge with
neural networks to improve the transparency and performance of their models.
However, these methods can only incorporate partial biological knowledge,
leading to suboptimal performance. In this paper, we propose the Biological
Factor Regulatory Neural Network (BFReg-NN), a generic framework to model
relations among biological factors in cell systems. BFReg-NN starts from gene
expression data and is capable of merging most existing biological knowledge
into the model, including the regulatory relations among genes or proteins
(e.g., gene regulatory networks (GRN), protein-protein interaction networks
(PPI)) and the hierarchical relations among genes, proteins and pathways (e.g.,
several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has
the ability to provide new biologically meaningful insights because of its
white-box characteristics. Experimental results on different gene
expression-based tasks verify the superiority of BFReg-NN compared with
baselines. Our case studies also show that the key insights found by BFReg-NN
are consistent with the biological literature