Listar por autor "Fernández-Lozano, Carlos"

Mostrando ítems 21-40 de 43

    • Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data 

      Liñares Blanco, Jose; Fernández-Lozano, Carlos; Seoane Fernández, José Antonio; López-Campos, Guillermo (NLM (Medline), 2021)
      [Abstract] In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in ...

    • Machine learning analysis of TCGA cancer data 

      Liñares Blanco, Jose; Pazos, A.; Fernández-Lozano, Carlos (PeerJ Inc., 2021)
      [Abstract] In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas ...

    • Machine Learning Analysis of the Human Infant Gut Microbiome Identifies Influential Species in Type 1 Diabetes 

      Fernández-Edreira, Diego; Liñares Blanco, Jose; Fernández-Lozano, Carlos (Elsevier, 2021)
      [Abstract] Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due ...

    • Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes 

      Liñares Blanco, Jose; Fernández-Lozano, Carlos; Seoane Fernández, José Antonio; López-Campos, Guillermo (FRONTIERS MEDIA S.A., 2022)
      [Abstract] Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple ...

    • Markov Mean Properties for Cell Death-Related Protein Classification 

      Fernández-Lozano, Carlos; Gestal, M.; 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 ...

    • MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products 

      Carracedo-Reboredo, Paula; Aranzamendi, Eider; He, Shan; Arrasate, Sonia; Munteanu, Cristian-Robert; Fernández-Lozano, Carlos; Sotomayor, Nuria; Lete, Esther; González-Díaz, Humberto (BMC, 2024-01-23)
      [Absctract]: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts ...

    • Molecular Docking and Machine Learning Analysis of Abemaciclib in Colon Cancer 

      Liñares Blanco, Jose; Munteanu, Cristian-Robert; Pazos, A.; Fernández-Lozano, Carlos (BioMed Central Ltd., 2020-07-08)
      [Abstract] Background - The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is ...

    • Music Recommendation System Based on Ratings Obtained from Amazon 

      Marcos Vázquez, Sergio; Fernández-Lozano, Carlos; Carballal, Adrián; Cedrón, Francisco (Universidade da Coruña, Servizo de Publicacións, 2023)
      [Abstract] In the current context of an era in which a significant portion of people are constantly living online, with various multimedia streaming platforms serving as major sources of entertainment, and with e-commerce ...

    • Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic Programming 

      Rivero, Daniel; Fernández-Blanco, Enrique; Fernández-Lozano, Carlos; Pazos, A. (Taylor & Francis Group, 2019-07-31)
      [Abstract] Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of ...

    • Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection 

      Liñares Blanco, Jose; 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, Jose; 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 ...

    • Random Forest-Based Prediction of Stroke Outcome 

      Fernández-Lozano, Carlos; Hervella, Pablo; Mato-Abad, Virginia; Rodríguez-Yáñez, Manuel; Suárez-Garaboa, Sonia; López Dequidt, Iria Alejandra; Estany-Gestal, Ana; Sobrino, Tomás; Campos, Francisco; Castillo, José; Rodríguez-Yáñez, Santiago; Iglesias Rey, Ramón (Nature Research, 2021)
      [Abstract] We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and ...

    • Redesign and performance of an automatic segmentation method 

      Carballal, Adrián; Fernández-Lozano, Carlos; Nóvoa, Francisco; 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 ...

    • Shiny Dashboard for Monitoring the COVID-19 Pandemic in Spain 

      Fernández-Lozano, Carlos; Cedrón, Francisco (MDPI AG, 2020-08-20)
      [Abstract] Real-time monitoring of events such as the recent pandemic caused by COVID-19, as well as the visualization of the effects produced by its expansion, has highlighted the need to join forces in fields already ...

    • Texture Analysis in Gel Electrophoresis Images Using an Integrative Kernel-Based Approach 

      Fernández-Lozano, Carlos; Seoane, José A.; Gestal, M.; 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 ...

    • Two-Dimensional Gel Electrophoresis Image Registration Using Block-Matching Techniques and Deformation Models 

      Rodriguez, Alvaro; Fernández-Lozano, Carlos; Dorado, Julián; Rabuñal, Juan R. (Elsevier, 2014-03-05)
      [Abstract] Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and ...

    • Using Genetic Algorithms to Improve Support Vector Regression in the Analysis of Atomic Spectra of Lubricant Oils 

      Fernández-Lozano, Carlos; Cedrón, Francisco; Rivero, Daniel; Dorado, Julián; Andrade-Garda, José Manuel; Pazos, A.; Gestal, M. (Emerald, 2016-06)
      [Abstract] Purpose – The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate ...

    • VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria 

      Fernández Edreira, Diego; Liñares Blanco, Jose; Vázquez del Río, Patricia; Fernández-Lozano, Carlos (Elsevier, 2024)
      [Abstract]: This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort ...