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Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems
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. Artificia...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098100/ https://www.ncbi.nlm.nih.gov/pubmed/30120369 http://dx.doi.org/10.1038/s41598-018-30637-w |
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author | Barreiro, Enrique Munteanu, Cristian R. Cruz-Monteagudo, Maykel Pazos, Alejandro González-Díaz, Humbert |
author_facet | Barreiro, Enrique Munteanu, Cristian R. Cruz-Monteagudo, Maykel Pazos, Alejandro González-Díaz, Humbert |
author_sort | Barreiro, Enrique |
collection | PubMed |
description | 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 Sh(k) 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. |
format | Online Article Text |
id | pubmed-6098100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-60981002018-08-23 Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems Barreiro, Enrique Munteanu, Cristian R. Cruz-Monteagudo, Maykel Pazos, Alejandro González-Díaz, Humbert Sci Rep Article 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 Sh(k) 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. Nature Publishing Group UK 2018-08-17 /pmc/articles/PMC6098100/ /pubmed/30120369 http://dx.doi.org/10.1038/s41598-018-30637-w Text en © The Author(s) 2018 Open Access 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/. |
spellingShingle | Article Barreiro, Enrique Munteanu, Cristian R. Cruz-Monteagudo, Maykel Pazos, Alejandro González-Díaz, Humbert Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title_full | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title_fullStr | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title_full_unstemmed | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title_short | Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems |
title_sort | net-net auto machine learning (automl) prediction of complex ecosystems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098100/ https://www.ncbi.nlm.nih.gov/pubmed/30120369 http://dx.doi.org/10.1038/s41598-018-30637-w |
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