Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks

Abstract The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer w...

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Autores principales:	Paolo Mignone, Gianvito Pio, Sašo Džeroski, Michelangelo Ceci
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3fa04fcf691b4bbcbf6dcf37d380ecce
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spelling	oai:doaj.org-article:3fa04fcf691b4bbcbf6dcf37d380ecce2021-12-02T12:42:19ZMulti-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks10.1038/s41598-020-78033-72045-2322https://doaj.org/article/3fa04fcf691b4bbcbf6dcf37d380ecce2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78033-7https://doaj.org/toc/2045-2322Abstract The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.Paolo MignoneGianvito PioSašo DžeroskiMichelangelo CeciNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paolo Mignone Gianvito Pio Sašo Džeroski Michelangelo Ceci Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
description	Abstract The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.
format	article
author	Paolo Mignone Gianvito Pio Sašo Džeroski Michelangelo Ceci
author_facet	Paolo Mignone Gianvito Pio Sašo Džeroski Michelangelo Ceci
author_sort	Paolo Mignone
title	Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
title_short	Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
title_full	Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
title_fullStr	Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
title_full_unstemmed	Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
title_sort	multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/3fa04fcf691b4bbcbf6dcf37d380ecce
work_keys_str_mv	AT paolomignone multitasklearningforthesimultaneousreconstructionofthehumanandmousegeneregulatorynetworks AT gianvitopio multitasklearningforthesimultaneousreconstructionofthehumanandmousegeneregulatorynetworks AT sasodzeroski multitasklearningforthesimultaneousreconstructionofthehumanandmousegeneregulatorynetworks AT michelangeloceci multitasklearningforthesimultaneousreconstructionofthehumanandmousegeneregulatorynetworks
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Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks

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