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Bio-AIMS collection of chemoinformatics web tools based on molecular graph information and artificial intelligence models 

Munteanu, Cristian-Robert; González-Díaz, Humberto; García, Rafael; Loza, Mabel; Pazos, A. (Bentham, 2015-09-01)
[Abstract] The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction ...
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Ambient Intelligence Systems for Personalized Sport Training 

Vales-Alonso, Javier; López-Matencio, Pablo; Gonzalez-Castaño, Francisco J.; Navarro-Hellín, Honorio; Baños-Guirao, Pedro J.; Pérez-Martínez, Francisco J.; Martínez-Álvarez, Rafael P.; González-Jiménez, Daniel; Gil-Castiñeira, Felipe; Duro, Richard J. (Molecular Diversity Preservation International, 2010)
Several research programs are tackling the use of Wireless Sensor Networks (WSN) at specific fields, such as e-Health, e-Inclusion or e-Sport. This is the case of the project “Ambient Intelligence Systems Support for ...
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On-line learning and anomaly detection methods : applications to fault assessment 

Martínez Rego, David (2013)
[Abstract] This work lays at the intersection of two disciplines, Machine Learning (ML) research and predictive maintenance of machinery. On the one hand, Machine Learning aims at detecting patterns in data gathered from ...
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Experimental study and random forest prediction model of microbiome cell surface hydrophobicity 

Liu, Yong; Tang, Shaoxun; Fernández-Lozano, Carlos; Munteanu, Cristian-Robert; Pazos, A.; Yu, Yi-zun; Tan, Zhiliang; González-Díaz, Humberto (Elsevier, 2016-11-09)
[Abstract] The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation ...
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A generalized linear model for cardiovascular complications prediction in PD patients 

Fernández-Lozano, Carlos; Alonso Valente, Rafael; Fidalgo Díaz, Manuel; Pazos, A. (ACM, 2018)
[Abstract] This study was conducted using machine learning models to identify patient non-invasive information for cardiovascular complications prediction in peritoneal dialysis patients. Nowadays is well known that ...
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Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics 

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M.; Loza, María Isabel; Munteanu, Cristian-Robert; Pazos, A.; García-Mera, Xerardo; González-Díaz, Humberto (American Chemical Society, 2018-05-23)
[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning ...
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Early Detection of Depression: Social Network Analysis and Random Forest Techniques 

Cacheda, Fidel; Fernández, Diego; Novoa, Francisco; Carneiro, Victor (J M I R Publications, Inc., 2019-06-10)
[Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, ...
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Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment 

Liñares Blanco, José; Fernández-Lozano, Carlos (M D P I AG, 2019-07-31)
[Abstract]The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of ...
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NLOS Identification and Mitigation Using Low-Cost UWB Devices 

Barral Vales, Valentín; Escudero, Carlos J.; García-Naya, José A.; Maneiro-Catoira, Roberto (M D P I AG, 2019-08-08)
[Abstract] Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate ...
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Authentication of tequilas using pattern recognition and supervised classification 

Pérez-Caballero, G.; Andrade Garda, José Manuel; Olmos, P.; Molina, Y.; Jiménez, I.; Durán, J.J.; Fernández-Lozano, Carlos; Miguel-Cruz, F. (Elsevier, 2017-07-18)
[Abstract] Sales of reputed, Mexican tequila grown substantially in last years and, therefore, counterfeiting is increasing steadily. Hence, methodologies intended to characterize and authenticate commercial beverages are ...
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AuthorFernández-Lozano, Carlos (16)Pazos, A. (16)Munteanu, Cristian-Robert (13)Gestal, M. (7)González-Díaz, Humberto (7)Dorado, Julián (6)Fernández-Blanco, Enrique (4)Liñares Blanco, José (4)Rivero, Daniel (4)Barral Vales, Valentín (3)... View MoreSubject
Machine learning (46)
Feature selection (5)Support vector machines (4)Aprendizaje automático (3)Artificial neural networks (3)Cancer (3)Data mining (3)Big Data (2)Bioinformatics (2)Compression error (2)... View MoreDate Issued2020 (10)2019 (14)2018 (4)2017 (3)2016 (6)2015 (3)2014 (2)2013 (2)2012 (1)2010 (1)
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