Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

Gianvito Pio, Paolo Mignone, Giuseppe Magazzù, Guido Zampieri, Michelangelo Ceci, Claudio Angione

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Abstract 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: Supplementary information Supplementary data are available at Bioinformatics online.
Original languageEnglish
Pages (from-to)487-493
Number of pages7
Issue number2
Early online date9 Sept 2021
Publication statusPublished - 15 Jan 2022

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© The Author(s) (2021). Published by Oxford University Press. All rights reserved. For Permissions, please email:


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