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Genes are fundamental for analyzing biological systems and many recent worksproposed to utilize gene expression for various biological tasks by deeplearning models. Despite their promising performance, it is hard for deepneural networks to provide biological insights for humans due to theirblack-box nature. Recently, some works integrated biological knowledge withneural 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 BiologicalFactor Regulatory Neural Network (BFReg-NN), a generic framework to modelrelations among biological factors in cell systems. BFReg-NN starts from geneexpression data and is capable of merging most existing biological knowledgeinto 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 hasthe ability to provide new biologically meaningful insights because of itswhite-box characteristics. Experimental results on different geneexpression-based tasks verify the superiority of BFReg-NN compared withbaselines. Our case studies also show that the key insights found by BFReg-NNare consistent with the biological literature.

Biological Factor Regulatory Neural Network