[1] J. E. Q. Ibarra, J. Á. G. Ordiano, J.-J. Flores-Godoy, y G. E. Vazquez, «Clustering of News Texts Using Hyperdimensional Computing», en 2024 IEEE URUCON, IEEE, nov. 2024, pp. 1-5. doi: 10.1109/urucon63440.2024.10850161.

[2] B. O. Petrazzini, H. Naya, F. Lopez-Bello, G. Vazquez, y L. Spangenberg, «Evaluation of different approaches for missing data imputation on features associated to genomic data», BioData Mining, vol. 14, n.º 1, sep. 2021, doi: 10.1186/s13040-021-00274-7.

[3] F. Cravero, S. A. Schustik, M. J. Martínez, G. E. Vázquez, M. F. Díaz, y I. Ponzoni, «Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RVDD Algorithm Using Synthetic Polydisperse Data Sets», Journal of Chemical Information and Modeling, vol. 60, n.º 2, pp. 592-603, 2020.

[4] I. Ponzoni et al., «QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease», Scientific Reports, vol. 9, n.º 1, jun. 2019, doi: 10.1038/s41598-019-45522-3.

[5] J. Murillo et al., «A Preliminary Comparison of P-Tool Consistency», en IFMBE Proceedings, Springer International Publishing, 2019, pp. 731-735. doi: 10.1007/978-3-030-30648-9_97.

[6] F. Cravero, M. J. Martinez, G. E. Vazquez, M. F. Diaz, y I. Ponzoni, «Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design», Journal of Integrative Bioinformatics - JIB, 2016, doi: 10.2390/BIECOLL-JIB-2016-286.

[7] F. Cravero, M. J. Martinez, G. E. Vazquez, M. F. Díaz, y I. Ponzoni, «Intelligent Systems for Predictive Modelling in Cheminformatics: QSPR Models for Material Design Using Machine Learning and Visual Analytics Tools», en 10th International Conference on Practical Applications of Computational Biology & Bioinformatics, Springer International Publishing, 2016, pp. 3-11. doi: 10.1007/978-3-319-40126-3_1.

[8] M. J. Martínez, I. Ponzoni, M. F. Díaz, G. E. Vazquez, y A. J. Soto, «Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods», Journal of Cheminformatics, vol. 7, n.º 1, ago. 2015, doi: 10.1186/s13321-015-0092-4.

[9] J. I. Ardenghi, G. E. Vazquez, y N. B. Brignole, «Parallel optimization by means of a Spectral-Projected-Gradient approach», Computers & Chemical Engineering, vol. 81, pp. 344-354, oct. 2015, doi: 10.1016/j.compchemeng.2015.04.010.

[10] D. Palomba, G. E. Vazquez, y M. F. Díaz, «Prediction of elongation at break for linear polymers», Chemometrics and Intelligent Laboratory Systems, vol. 139, pp. 121-131, dic. 2014, doi: 10.1016/j.chemolab.2014.09.009.

[11] D. Palomba et al., «Prediction of mechanical properties of tensile test for linear polymers. QSPR modeling with computational intelligence and interactive visual analysis,Predicción de propiedades mecanicas del ensayo de tensión para polímeros lineales. Modelado QSPR con inteligencia computacional y análisis visual interactivo», Journal of the Argentine Chemical Society, vol. 101, n.º 1-2, pp. 137-147, 2014.

[12] J. I. Ardenghi, G. E. Vazquez, y N. B. Brignole, «A Parallel Spectral-Projected-Gradient Method for Optimization in Process Engineering», en Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design, Elsevier, 2014, pp. 675-680. doi: 10.1016/b978-0-444-63433-7.50097-3.

[13] D. Palomba, G. E. Vazquez, y M. F. Díaz, «Novel descriptors from main and side chains of high-molecular-weight polymers applied to prediction of glass transition temperatures», Journal of Molecular Graphics and Modelling, vol. 38, pp. 137-147, sep. 2012, doi: 10.1016/j.jmgm.2012.04.006.

[14] D. Palomba, M. Martínez, I. Ponzoni, M. Díaz, G. Vazquez, y A. Soto, «QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge», Molecules, vol. 17, n.º 12, pp. 14937-14953, dic. 2012, doi: 10.3390/molecules171214937.

[15] G. Iraola, G. Vazquez, L. Spangenberg, y H. Naya, «Reduced Set of Virulence Genes Allows High Accuracy Prediction of Bacterial Pathogenicity in Humans», PLoS ONE, vol. 7, n.º 8, p. e42144, ago. 2012, doi: 10.1371/journal.pone.0042144.

[16] A. J. Soto, G. E. Vazquez, M. Strickert, y I. Ponzoni, «Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation», Molecular Informatics, vol. 30, n.º 9, pp. 779-789, ago. 2011, doi: 10.1002/minf.201100053.

[17] A. J. Soto, M. Strickert, G. E. Vazquez, y E. Milios, «Subspace Mapping of Noisy Text Documents», en Advances in Artificial Intelligence, Springer Berlin Heidelberg, 2011, pp. 377-383. doi: 10.1007/978-3-642-21043-3_45.

[18] M. Strickert, A. J. Soto, y G. E. Vazquez, «Adaptive matrix distances aiming at optimum regression subspaces», Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010, pp. 93-98, 2010.

[19] A. J. Soto, M. Strickert, y G. Vazquez, «Adaptive matrix metrics for molecular descriptor assessment in QSPR classification», Journal of Cheminformatics, vol. 2, n.º S1, may 2010, doi: 10.1186/1758-2946-2-s1-p47.

[20] A. J. Soto, I. Ponzoni, y G. E. Vazquez, «Segregating Confident Predictions of Chemicals’ Properties for Virtual Screening of Drugs», en Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, Springer Berlin Heidelberg, 2009, pp. 1005-1012. doi: 10.1007/978-3-642-02481-8_153.

[21] A. J. Soto, R. L. Cecchini, G. E. Vazquez, y I. Ponzoni, «Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach», QSAR & Combinatorial Science, vol. 28, n.º 11–12, pp. 1509-1523, dic. 2009, doi: 10.1002/qsar.200960053.

[22] A. J. Soto, R. L. Cecchini, G. E. Vazquez, y I. Ponzoni, «A Wrapper-Based Feature Selection Method for ADMET Prediction Using Evolutionary Computing», en Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Springer, Berlin, Heidelberg, 2008, pp. 188-199. doi: 10.1007/978-3-540-78757-0_17.

[23] A. J. Soto, R. L. Cecchini, G. E. Vazquez, y I. Ponzoni, «An evolutionary approach for feature selection applied to ADMET prediction», Inteligencia Artificial, vol. 12, n.º 37, pp. 55-63, 2008.

[24] A. C. Olivera, G. E. Vazquez, y N. B. Brignole, «Instrumentation design for an ammonia plant: Cad model capture», XXII Interamerican Congress of Chemical Engineering, CIIQ 2006 and V Argentinian Congress of Chemical Engineering, CAIQ 2006 - Innovation and Management for Sustainable Development, 2006.

[25] F. Asteasuain, J. A. Carballido, G. E. Vazquez, y I. Ponzoni, «Using Computational Intelligence and Parallelism to Solve an Industrial Design Problem», en Advances in Artificial Intelligence - IBERAMIA-SBIA 2006, Springer Berlin Heidelberg, 2006, pp. 188-197. doi: 10.1007/11874850_23.

[26] G. E. Vazquez, N. B. Brignole, S. Diaz, N. B. Brignole, y J. A. Bandoni, «Optimization of industrial problems using parallel processing under distributed environments», Chemical Engineering Communications, vol. 189, n.º 5, pp. 642-656, may 2002, doi: 10.1080/00986440211739.

[27] G. E. Vazquez, I. Ponzoni, M. C. Sánchez, y N. B. Brignole, «ModGen: a model generator for instrumentation analysis», Advances in Engineering Software, vol. 32, n.º 1, pp. 37-48, ene. 2001, doi: 10.1016/s0965-9978(00)00073-9.

[28] I. Ponzoni, G. E. Vazquez, M. C. Sánchez, y N. B. Brignole, «Parallel observability analysis on networks of workstations», Computers & Chemical Engineering, vol. 25, n.º 7–8, pp. 997-1002, ago. 2001, doi: 10.1016/s0098-1354(01)00625-1.

[29] G. E. Vazquez, R. Rainoldi, y N. B. Brignole, «Non-linear constrained GRG optimisation under parallel-distributed computing environments», Computer Aided Chemical Engineering, vol. 8, n.º C, pp. 127-132, 2000.

[30] G. E. Vazquez y N. B. Brignole, «Parallel NLP Strategies Using PVM on Heterogeneous Distributed Environments», en Recent Advances in Parallel Virtual Machine and Message Passing Interface, Springer Berlin Heidelberg, 1999, pp. 533-540. doi: 10.1007/3-540-48158-3_66.