Identifying the molecular mechanisms that control differential gene expression (DE) is a major goal of basic and disease biology. Here, we develop a systems biology model to predict DE and mine the biological basis of the factors that influence predicted gene expression to understand how it may be generated. This model, called DEcode, utilizes deep learning to predict DE based on genome-wide binding sites on RNAs and promoters. Ranking predictive factors from DEcode indicates that clinically relevant expression changes between thousands of individuals can be predicted mainly through the joint action of post-transcriptional RNA-binding factors. We also show the broad potential applications of DEcode to generate biological insights, by predicting DE between tissues, differential transcript usage, and drivers of ageing throughout the human lifespan, of gene co-expression relationships on a genome-wide scale, and of frequently differentially expressed genes across diverse conditions. DEcode is freely available to researchers to identify influential molecular mechanisms for any human expression data.

识别控制差异基因表达（DE）的分子机制是基础生物学和疾病生物学的主要目标。在这里，我们开发了一个系统生物学模型来预测DE，并挖掘影响预测基因表达的因素的生物学基础，以了解其可能如何产生。这种称为DEcode的模型利用深度学习根据RNA和启动子上全基因组结合位点预测DE。来自DEcode的排名预测因子排名表明，主要通过转录后RNA结合因子的联合作用，可以预测数千个人之间临床相关的表达变化。通过预测组织之间的DE，不同的转录本使用情况以及整个人类寿命中的衰老驱动因素，我们还展示了DEcode在产生生物学见解方面的广泛潜在应用，基因在整个基因组范围内的共表达关系，以及在不同条件下经常差异表达的基因。研究人员可以免费使用DEcode来确定任何人类表达数据的影响分子机制。