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Humbert Gonzalez Diaz   Professor  Senior Scientist or Principal Investigator 
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Humbert Gonzalez Diaz published an article in March 2019.
Top co-authors See all
Juan M. Ruso

173 shared publications

Department of Applied Physics University of Santiago de Compostela, 15782, Spain

Florencio M. Ubeira

138 shared publications

Laboratorio de Parasitología, Facultad de Farmacia, Santiago de Compostela, Spain

Esther Lete

124 shared publications

Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain

Nuria Sotomayor

55 shared publications

Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain

Gerardo M. Casañola-Martin

27 shared publications

Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada

171
Publications
366
Reads
26
Downloads
282
Citations
Publication Record
Distribution of Articles published per year 
(2002 - 2019)
Total number of journals
published in
 
28
 
Publications See all
Article 1 Read 0 Citations Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Me... Deyani Nocedo-Mena, Carlos Cornelio, María Del Rayo Camacho-... Published: 04 March 2019
Journal of Chemical Information and Modeling, doi: 10.1021/acs.jcim.9b00034
DOI See at publisher website PubMed View at PubMed
Article 0 Reads 0 Citations MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochond... Michael Gonzalez Durruthy, Silvana Manske Nunes, Juliane Ven... Published: 09 November 2018
Journal of Chemical Information and Modeling, doi: 10.1021/acs.jcim.8b00631
DOI See at publisher website
Article 0 Reads 1 Citation PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer Harbil Bediaga, Sonia Arrasate, Humbert González-Díaz Published: 21 September 2018
ACS Combinatorial Science, doi: 10.1021/acscombsci.8b00090
DOI See at publisher website
Article 2 Reads 0 Citations Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication Vincent Blay, Toshiyuki Yokoi, Humbert González-Díaz Published: 23 August 2018
Journal of Chemical Information and Modeling, doi: 10.1021/acs.jcim.8b00383
DOI See at publisher website
Article 0 Reads 0 Citations Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems Enrique Barreiro, Cristian R. Munteanu, Maykel Cruz-Monteagu... Published: 17 August 2018
Scientific Reports, doi: 10.1038/s41598-018-30637-w
DOI See at publisher website PubMed View at PubMed ABS Show/hide 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.
Article 6 Reads 0 Citations Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-P... Joana Ferreira Da Costa, David Silva, Olga Caamaño, José M. ... Published: 23 May 2018
ACS Chemical Neuroscience, doi: 10.1021/acschemneuro.8b00083
DOI See at publisher website
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