{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:26:48Z","timestamp":1785461208450,"version":"3.56.0"},"reference-count":70,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e24020141","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T08:09:20Z","timestamp":1642493360000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Michele","family":"Monti","sequence":"first","affiliation":[{"name":"RNA System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy"},{"name":"Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2096-3210","authenticated-orcid":false,"given":"Jonathan","family":"Fiorentino","sequence":"additional","affiliation":[{"name":"Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3046-5170","authenticated-orcid":false,"given":"Edoardo","family":"Milanetti","sequence":"additional","affiliation":[{"name":"Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy"},{"name":"Department of Physics, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-1404","authenticated-orcid":false,"given":"Giorgio","family":"Gosti","sequence":"additional","affiliation":[{"name":"Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy"},{"name":"Department of Physics, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7524-6310","authenticated-orcid":false,"given":"Gian Gaetano","family":"Tartaglia","sequence":"additional","affiliation":[{"name":"RNA System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy"},{"name":"Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain"},{"name":"Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy"},{"name":"Department of Biology and Biotechnology Charles Darwin, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.cell.2011.02.016","article-title":"Interactome Networks and Human Disease","volume":"144","author":"Vidal","year":"2011","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: A network-based approach to human disease","volume":"12","author":"Gulbahce","year":"2011","journal-title":"Nat. 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