J. M. Jerez

发表

Layer multiplexing FPGA implementation for deep back-propagation learning

Leonardo Franco, José M. Jerez, Francisco Ortega-Zamorano, 2017, Integr. Comput. Aided Eng..

Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks

J. M. Jerez, A. Lluch, A. Llombart-Cussac, 2005, Breast Cancer Research and Treatment.

Missing data imputation in breast cancer prognosis

Leonardo Franco, José Luis Subirats, José M. Jerez, 2006 .

Information in the first spike, the order of spikes, and the number of spikes provided by neurons in the inferior temporal visual cortex

E. Rolls, N. Aggelopoulos, L. Franco, 2006, Vision Research.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

R. M. Luque-Baena, L. Franco, J. M. Jerez, 2014, Theoretical Biology and Medical Modelling.

Solving Scheduling Problems with Genetic Algorithms Using a Priority Encoding Scheme

Héctor Mesa, Ignacio J. Turias, José Luis Subirats, 2017, IWANN.

A microRNA Signature Associated with Early Recurrence in Breast Cancer

J. M. Jerez, M. Claros, E. Alba, 2014, PloS one.

Concurrent radiotherapy plus epidermal growth factor receptor inhibitors in patients with human papillomavirus-related head and neck cancer

J. M. Jerez, M. D. Toledo, J. Trigo, 2014, Clinical and Translational Oncology.

Differential outcome of concurrent radiotherapy plus epidermal growth factor receptor inhibitors versus radiotherapy plus cisplatin in patients with human papillomavirus-related head and neck cancer

J. M. Jerez, E. Alba, M. D. Toledo, 2013, BMC Cancer.

Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex

Edmund T. Rolls, Leonardo Franco, José M. Jerez, 2007, Biological Cybernetics.

CBA Generated Receptive Fields Implemented in a Facial Expression Recognition Task

Leonardo Franco, José M. Jerez, Ignacio Molina, 2003, IWANN.

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

L. Franco, J. M. Jerez, E. Alba, 2020, European journal of cancer.

FPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model

Marcelo A. Montemurro, José M. Jerez, Francisco Ortega-Zamorano, 2016, IEEE Transactions on Parallel and Distributed Systems.

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data

Guillermo López-García, José M Jerez, Leonardo Franco, 2020, PloS one.

Triple negative breast cancer subtypes and pathologic complete response rate to neoadjuvant chemotherapy

Eva Carrasco, Emilio Alba, Ana Lluch, 2018, Oncotarget.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

Daniel Urda, José Luis Subirats, Emilio Alba, 2016, PloS one.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

R. M. Luque-Baena, L. Franco, J. M. Jerez, 2014, Theoretical Biology and Medical Modelling.

Analysis of Cancer Microarray Data using Constructive Neural Networks and Genetic Algorithms

Rafael Marcos Luque Baena, Daniel Urda, Leonardo Franco, 2013, IWBBIO.

Constructive Neural Networks to Predict Breast Cancer Outcome by Using Gene Expression Profiles

Daniel Urda, Leonardo Franco, José Luis Subirats, 2010, IEA/AIE.

A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions

Leonardo Franco, José Luis Subirats, José M. Jerez, 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

Optimal Synthesis of Boolean Functions by Threshold Functions

Leonardo Franco, José Luis Subirats, José M. Jerez, 2006, ICANN.

Male breast cancer: correlation between immunohistochemical subtyping and PAM50 intrinsic subtypes, and the subsequent clinical outcomes

J. M. Jerez, E. Alba, C. Ramírez-Tortosa, 2018, Modern Pathology.

Missing data imputation using statistical and machine learning methods in a real breast cancer problem

Leonardo Franco, José M. Jerez, Ignacio Molina, 2010, Artif. Intell. Medicine.

Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords

M. Gonzalo Claros, Rafael Marcos Luque Baena, Daniel Urda, 2014, J. Biomed. Informatics.

Extension of the Generalization Complexity Measure to Real Valued Input Data Sets

Leonardo Franco, José Luis Subirats, José M. Jerez, 2010, ISNN.

Neural Network Architecture Selection: Can Function Complexity Help?

Leonardo Franco, José M. Jerez, Iván Gómez, 2009, Neural Processing Letters.

WIMP: Web server tool for missing data imputation

D. Urda, J.L. Subirats, P.J. García-Laencina, 2012, Comput. Methods Programs Biomed..

Deep Neural Network Architecture Implementation on FPGAs Using a Layer Multiplexing Scheme

José M. Jerez, Francisco Ortega-Zamorano, Leonardo Franco, 2016, DCAI.

The Generalization Complexity Measure for Continuous Input Data

Leonardo Franco, Omar Osenda, Iván Gómez, 2014, TheScientificWorldJournal.

Pattern of recurrence of early breast cancer is different according to intrinsic subtype and proliferation index

J. M. Jerez, L. Franco, E. Alba, 2013, Breast Cancer Research.

Improving classification accuracy using data augmentation on small data sets

Leonardo Franco, José M. Jerez, Francisco J. Moreno-Barea, 2020, Expert Syst. Appl..

Supervised discretization can discover risk groups in cancer survival analysis

Leonardo Franco, José M. Jerez, Emilio Alba, 2016, Comput. Methods Programs Biomed..

Forward Noise Adjustment Scheme for Data Augmentation

Daniel Urda, José M. Jerez, Leonardo Franco, 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

Rafael Marcos Luque Baena, Daniel Urda, Leonardo Franco, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Different Pathological Complete Response Rates According to PAM50 Subtype in HER2+ Breast Cancer Patients Treated With Neoadjuvant Pertuzumab/Trastuzumab vs. Trastuzumab Plus Standard Chemotherapy: An Analysis of Real-World Data

J. M. Jerez, A. Prat, E. Alba, 2019, Front. Oncol..

Luminal androgen receptor role and pathological complete response rate to neoadjuvant chemotherapy in triple negative breast cancer

J. M. Jerez, J. Albanell, A. Prat, 2016 .

Optimal prediction of mortality after abdominal aortic aneurysm repair with statistical models (vol 43, pg 467, 2006)

Leonardo Franco, José M. Jerez, Iordanis E. Evangelou, 2006 .

Data Augmentation Meta-Classifier Scheme for imbalanced data sets

Francisco J. Moreno-Barea, L. Franco, J. M. Jerez, 2022, 2022 IEEE Symposium Series on Computational Intelligence (SSCI).

GAN-Based Data Augmentation for Prediction Improvement Using Gene Expression Data in Cancer

Francisco J. Moreno-Barea, J. M. Jerez, L. Franco, 2022, ICCS.

A self-organizing map to improve vehicle detection in flow monitoring systems

Ezequiel López-Rubio, Esteban J. Palomo, Rafael Marcos Luque Baena, 2015, Soft Comput..

Multiclass Pattern Recognition Extension for the New C-Mantec Constructive Neural Network Algorithm

Leonardo Franco, José Luis Subirats, José M. Jerez, 2010, Cognitive Computation.

C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons

Leonardo Franco, José Luis Subirats, José M. Jerez, 2012, Neural Networks.

FPGA Implementation of the C-Mantec Neural Network Constructive Algorithm

Leonardo Franco, José M. Jerez, Francisco Ortega-Zamorano, 2014, IEEE Transactions on Industrial Informatics.

Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks

Maria do Carmo Nicoletti, David A. Elizondo, Leonardo Franco, 2009, Constructive Neural Networks.

BLASSO: integration of biological knowledge into a regularized linear model

M. Gonzalo Claros, Daniel Urda, Leonardo Franco, 2018, BMC Systems Biology.

Machine learning models to search relevant genetic signatures in clinical context

Rafael Marcos Luque Baena, Daniel Urda, Noelia Sánchez-Maroño, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Early Breast Cancer Prognosis Prediction and Rule Extraction Using a New Constructive Neural Network Algorithm

Leonardo Franco, José Luis Subirats, José M. Jerez, 2007, IWANN.

FPGA Implementation of Neurocomputational Models: Comparison Between Standard Back-Propagation and C-Mantec Constructive Algorithm

L. Franco, Francisco Ortega-Zamorano, J. M. Jerez, 2017, Neural Processing Letters.

Artificial neural networks and prognosis in medicine. Survival analysis in breast cancer patients

Leonardo Franco, José M. Jerez, Emilio Alba, 2005, ESANN.

Active Learning Using a Constructive Neural Network Algorithm

Leonardo Franco, José Luis Subirats, José M. Jerez, 2008, ICANN.

Single-Drop Experiments for Challenging Conditions as Basis for Extraction-Column Simulations

A. Pfennig, J. M. Jerez, F. Buchbender, 2017 .

Serum protein levels following surgery in breast cancer patients: a protein microarray approach.

L. Franco, J. M. Jerez, E. Alba, 2012, International journal of oncology.

Addressing critical issues in the development of an Oncology Information System

Daniel Urda, Leonardo Franco, José Luis Subirats, 2013, Int. J. Medical Informatics.

PREDICTION OF CARBON MONOXIDE (CO) ATMOSPHERIC POLLUTION CONCENTRATIONS USING METEROLOGICAL VARIABLES

Ignacio J. Turias, Leonardo Franco, José M. Jerez, 2017 .

A Self-organizing Map for Traffic Flow Monitoring

Ezequiel López-Rubio, Esteban J. Palomo, Rafael Marcos Luque Baena, 2013, IWANN.

Classification of high dimensional data using LASSO ensembles

Daniel Urda, Leonardo Franco, José M. Jerez, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Thermal comfort estimation using a neurocomputational model

Francisco Ortega-Zamorano, José Rodríguez-Alabarce, José M. Jerez, 2016, 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI).