Prediction of time series gene expression and structural analysis of gene regulatory networks using recurrent neural networks

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• dc.contributor.author Monti, Michele
• dc.contributor.author Fiorentino, Jonathan
• dc.contributor.author Milanetti, Edoardo
• dc.contributor.author Gosti, Giorgio
• dc.contributor.author Tartaglia, Gian Gaetano
• dc.date.accessioned 2022-04-05T10:07:45Z
• dc.date.available 2022-04-05T10:07:45Z
• dc.date.issued 2022
• dc.description.abstract Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.
• dc.description.sponsorship This research study was funded by the European Research Council grant ASTRA, number 855923; and grant INFORE, number 25080. MM was funded by INFORE, number 25080
• dc.format.mimetype application/pdf
• dc.identifier.citation Monti M, Fiorentino J, Milanetti E, Gosti G, Tartaglia GG. Prediction of time series gene expression and structural analysis of gene regulatory networks using recurrent neural networks. Entropy (Basel). 2022 Jan 18;24(2):141. DOI: 10.3390/e24020141
• dc.identifier.doi http://dx.doi.org/10.3390/e24020141
• dc.identifier.issn 1099-4300
• dc.identifier.uri http://hdl.handle.net/10230/52831
• dc.language.iso eng
• dc.publisher MDPI
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/855923
• dc.rights © 2022 by Michele Monti et al. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.other Genètica
• dc.subject.other Expressió gènica
• dc.title Prediction of time series gene expression and structural analysis of gene regulatory networks using recurrent neural networks
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion