Listar GI-RNASA - Artigos por autor "González-Díaz, Humberto"
Mostrando ítems 21-31 de 31
-
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 ... -
MIANN models of networks of biochemical reactions, ecosystems, and U.S. Supreme Court with Balaban-Markov indices
Duardo-Sánchez, Aliuska; González-Díaz, Humberto; Pazos, A. (Bentham Science, 2015)[Abstract] We can use Artificial Neural Networks (ANNs) and graph Topological Indices (TIs) to seek structure-property relationship. Balabans’ J index is one of the classic TIs for chemo-informatics studies. We used here ... -
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 ... -
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 ... -
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 ... -
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 ... -
The Rücker–Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics
González-Díaz, Humberto; Riera-Fernández, Pablo; Pazos, A.; Munteanu, Cristian-Robert (Elsevier, 2013-02-23)[Abstract] Rücker's walk count (WC) indices are well-known topological indices (TIs) used in Chemoinformatics to quantify the molecular structure of drugs represented by a graph in Quantitative structure–activity/property ...