Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems
dc.contributor.author | Barreiro, Enrique | |
dc.contributor.author | Munteanu, Cristian R. | |
dc.contributor.author | Cruz-Monteagudo, Maykel | |
dc.contributor.author | Pazos, Alejandro | |
dc.contributor.author | González Díaz, Humberto | |
dc.date.accessioned | 2018-11-22T13:45:32Z | |
dc.date.available | 2018-11-22T13:45:32Z | |
dc.date.issued | 2018-08-17 | |
dc.identifier.citation | Scientific Reports 8 : (2018) // Article ID 12340 | es_ES |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10810/29752 | |
dc.description.abstract | Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms. | es_ES |
dc.description.sponsorship | The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature Publishing | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/UNLC08-1E-002 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/UNLC13-13-3503 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | legal-social networks | es_ES |
dc.subject | information-content | es_ES |
dc.subject | organic-molecules | es_ES |
dc.subject | functional-organization | es_ES |
dc.subject | metabolic reactions | es_ES |
dc.subject | human-disease | es_ES |
dc.subject | miann models | es_ES |
dc.subject | qsar models | es_ES |
dc.subject | parasitology | es_ES |
dc.subject | indexes | es_ES |
dc.title | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.nature.com/articles/s41598-018-30637-w | es_ES |
dc.identifier.doi | 10.1038/s41598-018-30637-w | |
dc.departamentoes | Química orgánica II | es_ES |
dc.departamentoeu | Kimika organikoa II | es_ES |
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