Now showing items 1-20 of 22

    • 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 ...
    • A methodology for the design of experiments in computational intelligence with multiple regression models 

      Fernández-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian-Robert; Dorado, Julián; Pazos, A. (Peer J, 2016-12-01)
      [Abstract] The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the ...
    • A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer 

      Alonso-Calvo, Raúl; Paraíso-Medina, Sergio; Pérez-Rey, David; Alonso-Oset, Enrique; Stiphout, Ruud van; Taylor, Marian; Buffa, Francesca; Fernández-Lozano, Carlos; Pazos, A.; Maojo, Víctor (Elsevier, 2017-06-05)
      [Abstract] Introduction. The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient ...
    • Applied computational techniques on schizophrenia using genetic mutations 

      Aguiar-Pulido, Vanessa; Gestal, Marcos; Fernández-Lozano, Carlos; Rivero, Daniel; Munteanu, Cristian-Robert (Bentham, 2013-03-01)
      [Abstract] Schizophrenia is a complex disease, with both genetic and environmental influence. Machine learning techniques can be used to associate different genetic variations at different genes with a (schizophrenic or ...
    • 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 ...
    • Automatic multiscale vascular image segmentation algorithm for coronary angiography 

      Carballal, Adrián; Novoa, Francisco J.; Fernández-Lozano, Carlos; García-Guimaraes, Marcos; Aldama López, Guillermo; Calviño-Santos, Ramón; Vázquez-Rodríguez, José Manuel; Pazos, A. (Elsevier, 2018-09)
      [Abstract] Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of ...
    • Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data 

      Munteanu, Cristian-Robert; Fernández-Lozano, Carlos; Mato-Abad, Virginia; Pita-Fernández, Salvador; Álvarez-Linera, Juan; Hernández-Tamames, Juan Antonio; Pazos, A. (Elsevier, 2015-03-30)
      [Abstract] Several magnetic resonance techniques have been proposed as non-invasive imaging biomarkers for the evaluation of disease progression and early diagnosis of Alzheimer’s Disease (AD). This work is the first ...
    • Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota 

      Liu, Yong; Munteanu, Cristian-Robert; Fernández-Lozano, Carlos; Pazos, A.; Ran, Tao; Tan, Zhiliang; Zhou, Chuanshe; Tang, Shaoxun; González-Díaz, Humberto (Frontiers Science, 2017-06-30)
      [Abstract] The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels ...
    • 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 ...
    • Hybrid model based on genetic algorithms and SVM applied to variable selection within fruit juice classification 

      Fernández-Lozano, Carlos; Canto, C.; Gestal, Marcos; Andrade Garda, José Manuel; Rabuñal, Juan R.; Dorado, Julián; Pazos, A. (Hindawi, 2013-10-21)
      [Abstract] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines ...
    • Improving enzyme regulatory protein classification by means of SVM-RFE feature selection 

      Fernández-Lozano, Carlos; Fernández-Blanco, Enrique; Dave, Kirtan; Pedreira Souto, Nieves; Gestal, Marcos; Dorado, Julián; Munteanu, Cristian-Robert (Royal Society of Chemistry, 2014-01-14)
      [Abstract] Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification ...
    • Integrative multi-omics data-driven approach for metastasis prediction in cancer 

      Fernández-Lozano, Carlos; Liñares Blanco, José; Gestal, Marcos; Dorado, Julián; Pazos, A. (ACM, 2018)
      [Abstract] Nowadays biomedical research is generating huge amounts of omic data, covering all levels of genetic information from nucleotide sequencing to protein metabolism. In the beginning, data were analyzed independently ...
    • Kernel-based feature selection techniques for transport proteins based on star graph topological indices 

      Fernández-Lozano, Carlos; Gestal, Marcos; Pedreira Souto, Nieves; Postelnicu, Lucian; Dorado, Julián; Munteanu, Cristian-Robert (Bentham, 2013)
      [Abstract] The transport of the molecules inside cells is a very important topic, especially in Drug Metabolism. The experimental testing of the new proteins for the transporter molecular function is expensive and inefficient ...
    • Markov mean properties for cell death-related protein classification 

      Fernández-Lozano, Carlos; Gestal, Marcos; González-Díaz, Humberto; Dorado, Julián; Pazos, A.; Munteanu, Cristian-Robert (Elsevier, 2014-01-31)
      [Abstract] The cell death (CD) is a dynamic biological function involved in physiological and pathological processes. Due to the complexity of CD, there is a demand for fast theoretical methods that can help to find new ...
    • Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection 

      Liñares Blanco, José; Porto-Pazos, Ana B.; Pazos, A.; Fernández-Lozano, Carlos (Nature, 2018-10-24)
      [Abstract] Screening and in silico modeling are critical activities for the reduction of experimental costs. They also speed up research notably and strengthen the theoretical framework, thus allowing researchers to ...
    • 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 ...
    • Redesign and performance of an automatic segmentation method 

      Carballal, Adrián; Fernández-Lozano, Carlos; Novoa, Francisco J.; Rodríguez-Fernández, Nereida; Santos, Iria; García-Guimaraes, Marcos; Aldama López, Guillermo; Calviño-Santos, Ramón; Vázquez-Rodríguez, José Manuel; Pazos, A. (MDPI, 2018-09-05)
      [Resumen] La información más relevante de las angiografías coronarias se extrae empleando técnicas de segmentación, que pueden ser automáticas, semiautomáticas o manuales. Existen numerosos algoritmos de segmentación ...
    • Texture analysis in gel electrophoresis images using an integrative kernel-based approach 

      Fernández-Lozano, Carlos; Seoane, José A.; Gestal, Marcos; Gaunt, Tom R.; Dorado, Julián; Pazos, A.; Campbell, Colin (Nature, 2016-01-13)
      [Abstract] Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several ...
    • Texture classification of proteins using support vector machines and bio-inspired metaheuristics 

      Seoane, José A.; Mesejo, Pablo; Nashed, Youssef S.; Cagnoni, Stefano; Dorado, Julián; Fernández-Lozano, Carlos (Springer, 2014-11-02)
      [Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for ...
    • Técnicas basadas en kernel para el análisis de texturas en imagen biomédica 

      Fernández-Lozano, Carlos (2014)
      [Resumen] En problemas del mundo real es relevante el estudio de la importancia de todas las variables obtenidas de manera que sea posible la eliminación de ruido, es en este punto donde surgen las técnicas de selección ...