Motivation Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. Results We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature. Availability and implementation The method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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整合基因组规模的代谢建模和迁移学习以重建人类基因调控网络

动机基因调控负责控制众多生理功能并动态响应环境波动。因此，重建基因调控相互作用的人类网络对于理解跨细胞类型的细胞功能组织以及阐明致病过程和识别分子药物靶点至关重要。尽管在这个方向上付出了巨大的努力，但现有的计算方法主要依赖于基因表达水平，可能忽略了机械生化知识所传达的信息。此外，除了最近的一些尝试外，大多数现有方法仅考虑被分析生物体的信息，而没有利用相关模式生物体的信息。结果 我们提出了一种重建人类基因调控网络的新方法，该方法基于协同利用来自人类和小鼠的信息的迁移学习策略，由基因表达数据在计算机中生成的基因相关代谢特征传达。具体来说，我们从通过人工基因敲除的组织特异性代谢模型推断的代谢活动中学习预测模型。我们的实验表明，将我们的迁移学习方法与构建的代谢特征相结合，在重建准确性方面提供了显着优势，并为每个构建的代谢特征的贡献提供了额外的线索。可用性和实施​​ 本研究中获得的方法、数据集和所有结果可在以下网址获得：https://doi.org/10.6084/m9.figshare.c。