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Complex networks and machine learning: From molecular to social sciences
OtherAbstract: Combining complex networks analysis methods with machine learning (ML) algorithms have become a veryPalabras claves:Biological networks, Clustering, complex networks, Connectome, Ensemble classification, Machine learning, Neural networks, Social and economic networks, Supervised and unsupervised learning, SUPPORT VECTOR MACHINES, Systems biology, TIME SERIESAutores:Duardo-Sanchez A., Fletcher T., González‐díaz H., Maykel Cruz-Monteagudo, Quesada D.Fuentes:scopusCarbon nanotubes’ effect on mitochondrial oxygen flux dynamics: Polarography experimental study and machine learning models using star graph trace invariants of raman spectra
ArticleAbstract: This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) undPalabras claves:carbon nanotubes, Cytotoxicity, Graph Theory, Mitochondria oxygen mass flux, Raman spectroscopy, Spectral momentsAutores:Barreiro Sorrivas J.M., Gerardo M. Casañola-Martin, González-Durruthy M., González‐díaz H., Maojo V., Monserrat J.M., Munteanu C.R., Paraíso-Medina S., Rasulev B., Sierra A.P.Fuentes:scopusMulti-output model with Box–Jenkins operators of linear indices to pbkp_redict multi-target inhibitors of ubiquitin–proteasome pathway
ArticleAbstract: The ubiquitin–proteasome pathway (UPP) plays an important role in the degradation of cellular proteiPalabras claves:CHEMBL, Moving averages, Multi-scale and multi-output models, multi-target, QSAR, Ubiquitin–proteasome pathway inhibitorsAutores:Abad C., Gerardo M. Casañola-Martin, González‐díaz H., Merino-Sanjuán M., Pérez-Giménez F., Thu H.L.T., Yovani Marrero-PonceFuentes:scopusMulti-output model with box-jenkins operators of quadratic indices for pbkp_rediction of malaria and cancer inhibitors targeting ubiquitin-proteasome pathway (UPP) proteins
ArticleAbstract: The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory pPalabras claves:Atom-based quadratic indices, Cáncer, CHEMBL, MALARIA, Moving average, Multi-scale and multi-output model, multi-target, QSAR, UPP inhibitorAutores:Abad C., Gerardo M. Casañola-Martin, González‐díaz H., Merino-Sanjuán M., Pérez-Giménez F., Thu H.L.T., Yovani Marrero-PonceFuentes:scopusMultioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds
ArticleAbstract: Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceuticPalabras claves:antiretroviral compounds, BIG DATA, CHEMBL, Machine learning, perturbation theoryAutores:Eduardo Tejera, Emilia Vásquez-Domínguez, González‐díaz H., Vinicio Danilo Armijos-JaramilloFuentes:googlescopusNet-Net Auto Machine Learning (AutoML) Pbkp_rediction of Complex Ecosystems
ArticleAbstract: Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relPalabras claves:Autores:Barreiro E., González‐díaz H., Maykel Cruz-Monteagudo, Munteanu C.R., Sierra A.P.Fuentes:scopusNew experimental and computational tools for drug discovery: Medicinal chemistry, molecular docking, and machine learning - Part-VI
OtherAbstract:Palabras claves:Autores:González‐díaz H., Maykel Cruz-MonteagudoFuentes:scopusMachine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds
ArticleAbstract: Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. HPalabras claves:antibacterial compounds, CHEMBL, complex networks, Information Fusion, Machine learning, multidrug-resistant, perturbation theoryAutores:Gerardo M. Casañola-Martin, González‐díaz H., Green J.R., Karel Diéguez-Santana, Rasulev B., Roldán Torres GutiérrezFuentes:googlescopusPerturbation-theory machine learning (PTML) multilabel model of the CheMBL dataset of preclinical assays for antisarcoma compounds
ArticleAbstract: Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcPalabras claves:Autores:Alejandro Cabrera-Andrade, Andrés López-Cortés, Arrasate S., Eduardo Tejera, González‐díaz H., Munteanu C.R., Sierra A.P., Yunierkis Perez-CastilloFuentes:googlescopusPbkp_redicting metabolic reaction networks with Perturbation-Theory Machine Learning (PTML) models
ArticleAbstract: Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) modePalabras claves:Combinatorial perturbation theory models, complex networks, Linear invariants, Machine learning, Markov chains, Metabolic pathwaysAutores:Gerardo M. Casañola-Martin, González‐díaz H., Green J.R., Karel Diéguez-Santana, Rasulev B.Fuentes:googlescopus