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Juan Enrique Sánchez Lasheras
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2020 – today
- 2021
- [j3]Juan Enrique Sánchez Lasheras, Fernando Sánchez Lasheras, Carmen González-Donquiles, Adonina Tardón, Gemma Castaño-Vinyals, Camilo Palazuelos, Dolores Salas, Vicente Martín Sánchez, Francisco Javier de Cos Juez:
Hybrid algorithm for the classification of prostate cancer patients of the MCC-Spain study based on support vector machines and genetic algorithms. Neurocomputing 452: 386-394 (2021) - 2020
- [j2]Juan Enrique Sánchez Lasheras, Carmen González-Donquiles, Paulino José García Nieto, José Juan Jiménez Moleon, Dolores Salas-Trejo, Sergio Luis Suárez Gómez, Antonio J. Molina de la Torre, Joaquín González-Nuevo, Laura Bonavera, Jorge Carballido-Landeira, Francisco Javier de Cos Juez:
A methodology for detecting relevant single nucleotide polymorphism in prostate cancer with multivariate adaptive regression splines and backpropagation artificial neural networks. Neural Comput. Appl. 32(5): 1231-1238 (2020)
2010 – 2019
- 2019
- [j1]Juan Enrique Sánchez Lasheras, Sergio Luis Suárez Gómez, Jesús Daniel Santos Rodríguez, Gemma Castaño-Vinyals, Beatriz Pérez-Gómez, Adonina Tardón:
A multivariate regression approach for identification of SNPs importance in prostate cancer. J. Exp. Theor. Artif. Intell. 31(6): 817-828 (2019) - 2018
- [c2]Juan Enrique Sánchez Lasheras, Fernando Sánchez Lasheras, Carmen González-Donquiles, Adonina Tardón, Gemma Castaño Vynals, Beatriz Pérez-Gómez, Camilo Palazuelos, Dolors Sala, Francisco Javier de Cos Juez:
Classification of Prostate Cancer Patients and Healthy Individuals by Means of a Hybrid Algorithm Combing SVM and Evolutionary Algorithms. HAIS 2018: 547-557 - 2017
- [c1]Juan Enrique Sánchez Lasheras, Adonina Tardón, Guillermo González Tardón, Sergio Luis Suárez Gómez, Vicente Martín Sánchez, Carmen González-Donquiles, Francisco Javier de Cos Juez:
A Methodology for the Detection of Relevant Single Nucleotide Polymorphism in Prostate Cancer by Means of Multivariate Adaptive Regression Splines and Backpropagation Artificial Neural Networks. SOCO-CISIS-ICEUTE 2017: 391-399
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