Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

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....

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Autores principales:	Enrique Barreiro, Cristian R. Munteanu, Maykel Cruz-Monteagudo, Alejandro Pazos, Humbert González-Díaz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca4d40e5e9814d67ae3c38e05284fd9a
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spelling	oai:doaj.org-article:ca4d40e5e9814d67ae3c38e05284fd9a2021-12-02T11:41:15ZNet-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems10.1038/s41598-018-30637-w2045-2322https://doaj.org/article/ca4d40e5e9814d67ae3c38e05284fd9a2018-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-30637-whttps://doaj.org/toc/2045-2322Abstract 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 (Shk) 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.Enrique BarreiroCristian R. MunteanuMaykel Cruz-MonteagudoAlejandro PazosHumbert González-DíazNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Enrique Barreiro Cristian R. Munteanu Maykel Cruz-Monteagudo Alejandro Pazos Humbert González-Díaz Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems
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 (Shk) 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.
format	article
author	Enrique Barreiro Cristian R. Munteanu Maykel Cruz-Monteagudo Alejandro Pazos Humbert González-Díaz
author_facet	Enrique Barreiro Cristian R. Munteanu Maykel Cruz-Monteagudo Alejandro Pazos Humbert González-Díaz
author_sort	Enrique Barreiro
title	Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems
title_short	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_sort	net-net auto machine learning (automl) prediction of complex ecosystems
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/ca4d40e5e9814d67ae3c38e05284fd9a
work_keys_str_mv	AT enriquebarreiro netnetautomachinelearningautomlpredictionofcomplexecosystems AT cristianrmunteanu netnetautomachinelearningautomlpredictionofcomplexecosystems AT maykelcruzmonteagudo netnetautomachinelearningautomlpredictionofcomplexecosystems AT alejandropazos netnetautomachinelearningautomlpredictionofcomplexecosystems AT humbertgonzalezdiaz netnetautomachinelearningautomlpredictionofcomplexecosystems
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Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

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