Listar por autor "Munteanu, Cristian-Robert"
Mostrando ítems 41-60 de 64
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Mejora continua de la calidad de la docencia a partir del análisis de los resultados de evaluación
Gestal, M.; Fernán, Carlos; Munteanu, Cristian-Robert; Rabuñal, Juan R.; Dorado, Julián (Asociación de Enseñantes Universitarios de la Informática (AENUI), 2018)[Resumen] El objetivo de cualquier docente debería ser la mejora continua en sus materias. En este trabajo se muestra una aproximación para adecuar las enseñanzas a aquellos aspectos más necesarios dentro de una materia. ... -
MIANN models in medicinal, physical and organic chemistry
González-Díaz, Humberto; Arrasate, Sonia; Sotomayor, Nuria; Lete, Esther; Munteanu, Cristian-Robert; Pazos, A.; Besada-Porto, Lina; Ruso, Juan M. (Bentham, 2013)[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational ... -
Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors
Duardo-Sánchez, Aliuska; Munteanu, Cristian-Robert; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, A.; González-Díaz, Humberto (American Chemical Society, 2013-12-08)[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein ... -
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 ... -
Nanoinformatics: Developing New Computing Applications for Nanomedicine
Maojo, Víctor; Fritts, Martin; Martín Sánchez, Fernando; Iglesia, Diana de la; Cachau, Raul E.; García-Remesal, Miguel; Crespo, José; Mitchell, Joyce A.; Anguita, Alberto; Baker, Nathan; Barreiro, José María; Benítez, Sonia E.; Calle, Guillermo de la; Facelli, Julio C.; Ghazal, Peter; Geissbuhler, Antoine; González-Nilo, Fernando; Graf, Norbert; Grangeat, Pierre; Hermosilla, Isabel; Hussein, Rada; Kern, Josipa; Koch, Sabine; Legre, Yannick; López-Alonso, Victoria; López-Campos, Guillermo; Milanesi, Luciano; Moustakis, Vassilis; Munteanu, Cristian-Robert; Otero, Paula; Pazos, A.; Pérez-Rey, David; Potamias, George; Sanz, Ferrán; Kulikowski, Casimir (Springer, 2012)Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. ... -
Net-net Auto machine learning (AutoML) prediction of complex ecosystems
Barreiro, Enrique; Munteanu, Cristian-Robert; Cruz-Monteagudo, Maykel; Pazos, A.; González-Díaz, Humberto (Nature, 2018-08-17)[Abstract] Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume ... -
Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction
Barreiro, Enrique; Munteanu, Cristian-Robert; Gestal, M.; Rabuñal, Juan R.; Pazos, A.; González-Díaz, Humberto; Dorado, Julián (MDPI, 2020-02-14)[Abstract] Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs ... -
Obtendo información útil para a mellora dunha materia a partir dos resultados dos exames de resposta múltiple
Gestal, M.; Munteanu, Cristian-Robert; Rabuñal, Juan R.; Dorado, Julián (Universidade da Coruña, Cufie, 2019)[Resumo] Os procesos de avaliación, deben aplicarse ós docentes e mesmo á materia en si, non só ós alumnos. Con esta finalidade formúlase unha análise dos resultados acadados polo alumnado durante a proba de avaliación ... -
OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine
López-Cortés, Andrés; Paz-y-Miño, César; Guerrero, Santiago; Cabrera-Andrade, Alejandro; Munteanu, Cristian-Robert; González-Díaz, Humberto; Pazos, A.; Pérez-Castillo, Yunierkis; Tejera, Eduardo; Barigye, Stephen J. (Springer Nature, 2020-03-24)[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic ... -
Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics
Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M.; Loza, María Isabel; Munteanu, Cristian-Robert; Pazos, A.; García-Mera, Xerardo; González-Díaz, Humberto (American Chemical Society, 2018-05-23)[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning ... -
Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds
Cabrera-Andrade, Alejandro; López-Cortés, Andrés; Munteanu, Cristian-Robert; Pazos, A.; Pérez-Castillo, Yunierkis; Tejera, Eduardo; Arrasate, Sonia; González-Díaz, Humberto (American Chemical Society, 2020-10-14)[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple ... -
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
Munteanu, Cristian-Robert; Gutiérrez-Asorey, Pablo; Blanes-Rodríguez, Manuel; Hidalgo-Delgado, Ismael; Blanco Liverio, María de Jesús; Galdo, Brais; Porto-Pazos, Ana B.; Gestal, M.; Arrasate, Sonia; González-Díaz, Humberto (MDPI, 2021)[Abstract] The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built ... -
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
Urista, Diana V.; Carrué, Diego B.; Otero, Iago; Arrasate, Sonia; Quevedo‐Tumailli, Viviana F.; Gestal, M.; González-Díaz, Humberto; Munteanu, Cristian-Robert (MDPI, 2020-07)[Abstract]: Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed ... -
Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks
López-Cortés, Andrés; Cabrera-Andrade, Alejandro; Vázquez-Naya, José; Pazos, A.; Gonzáles-Díaz, Humberto; Paz-y-Miño, César; Guerrero, Santiago; Pérez-Castillo, Yunierkis; Tejera, Eduardo; Munteanu, Cristian-Robert (Springer Nature, 2020-05-22)[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. ... -
Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy
Jimenes-Vargas, Karina; Pazos, A.; Munteanu, Cristian-Robert; Pérez-Castillo, Yunierkis; Tejera, Eduardo (BMC, 2024-03-07)[Absctract]: For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed ... -
Prediction of Nucleoitide Binding Peptides Using Star Graph Topological Índices
Liu, Yong; Munteanu, Cristian-Robert; Fernández-Blanco, Enrique; Tan, Zhiliang; Santos-del-Riego, Antonino; Pazos, A. (Elsevier, 2015-08-05)[Abstract] The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this ... -
Random Forest Classification Based on Star Graph Topological Indices for Antioxidant Proteins
Fernández-Blanco, Enrique; Aguiar-Pulido, Vanessa; Munteanu, Cristian-Robert; Dorado, Julián (Elsevier, 2012-10-29)[Abstract] Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality ... -
Rudeva: una herramienta para la implantación de rúbricas de evaluación
Carballa, Roberto; Gestal, M.; Munteanu, Cristian-Robert; Rabuñal, Juan R.; Dorado, Julián (Universidade da Coruña, Cufie, 2020)[Resumen]: En el proceso educativo, uno de los procesos más importantes en el proceso educativo y, a la vez más costosos en cuanto al tiempo dedicado es el relativo a la evaluación (de la Garza, 2004). Sobre todo cuando ... -
S2SNet: a tool for transforming characters and numeric sequences into star network topological indices in chemoinformatics, bioinformatics, biomedical, and social-legal sciences
Munteanu, Cristian-Robert; Magalhaes, Alexandre L.; Duardo-Sánchez, Aliuska; Pazos, A.; González-Díaz, Humberto (Bentham, 2013)[Abstract] The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This allows the numerical quantification of the significant information ... -
SNOMED2HL7: a tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model
Pérez-Rey, David; Alonso-Calvo, Raúl; Paraíso-Medina, Sergio; Munteanu, Cristian-Robert; García-Remesal, Miguel (Elsevier, 2017-10)[Abstract] BACKGROUND: Current clinical research and practice requires interoperability among systems in a complex and highly dynamic domain. There has been a significant effort in recent years to develop integrative common ...