Department of Computer Science, University of Bari Aldo Moro, Bari 70125, Italy.
Big Data Lab, National Interuniversity Consortium for Informatics (CINI), Rome 00185, Italy.
Bioinformatics. 2022 Jan 3;38(2):487-493. doi: 10.1093/bioinformatics/btab647.
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.
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.
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 data are available at Bioinformatics online.
基因调控负责控制众多生理功能，并对环境波动做出动态响应。因此，构建人类基因调控相互作用网络对于理解跨细胞类型的细胞功能组织，以及阐明发病机制和识别分子药物靶点至关重要。尽管已经做出了巨大的努力，但现有的计算方法主要依赖于基因表达水平，可能忽略了由机制生化知识所传递的信息。此外，除了少数最近的尝试之外，大多数现有的方法仅考虑正在分析的生物体的信息，而没有利用相关模式生物体的信息。
我们提出了一种基于转移学习策略的人类基因调控网络重建方法，该策略协同利用了来自人类和小鼠的信息，这些信息由从基因表达数据中生成的与基因相关的代谢特征来传递。具体来说，我们从通过人工基因敲除的组织特异性代谢建模推断出的代谢活性中学习预测模型。我们的实验表明，我们的转移学习方法与构建的代谢特征相结合，在重建准确性方面具有显著优势，并且为每个构建的代谢特征的贡献提供了额外的线索。
该方法、数据集和本研究中获得的所有结果均可在 https://doi.org/10.6084/m9.figshare.c.5237687 上获得。
补充数据可在“Bioinformatics”在线获取。
