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dc.contributor.authorBarreiro, Enrique
dc.contributor.authorMunteanu, Cristian R.
dc.contributor.authorGestal, Marcos
dc.contributor.authorRabuñal, Juan Ramón
dc.contributor.authorPazos, Alejandro
dc.contributor.authorGonzález Díaz, Humberto
dc.contributor.authorDorado, Julián
dc.date.accessioned2020-03-03T18:46:54Z
dc.date.available2020-03-03T18:46:54Z
dc.date.issued2020-02-14
dc.identifier.citationApplied Sciences 10(4): (2020) // Article ID 1308es_ES
dc.identifier.issn2076-3417
dc.identifier.issn10.3390/app10041308
dc.identifier.urihttp://hdl.handle.net/10810/41926
dc.description.abstractBrain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal—Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 ± 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository.es_ES
dc.description.sponsorshipThe authors acknowledge Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.”). Authors also acknowledge the Basque Government (Eusko Jaurlaritza) grant (IT1045-16)—2016–2021 for consolidated research groups. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16, the “Drug Discovery Galician Network” Ref. ED431G/01, the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness through the project BIA2017-86738-R and 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. Additional support was offered by the Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds. Last, the authors also acknowledge research grants from the Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P) and support of Ikerbasque, the Basque Foundation for Science.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/CTQ2016-74881-Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectartificial neural networkses_ES
dc.subjectbrain connectome networkses_ES
dc.subjectmachine learninges_ES
dc.subjectNet-Net AutoMLes_ES
dc.titleNet-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Predictiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2020-03-02T12:41:41Z
dc.rights.holder© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/4/1308es_ES
dc.departamentoesQuímica orgánica IIes_ES
dc.departamentoeuKimika organikoa IIes_ES


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).