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A Fuzzy System Classification Approach for QSAR Modeling of αAmylase and α-Glucosidase Inhibitors
ArticleAbstract: Introduction: This report proposes the application of a new Machine Learning algorithm called FuzzyPalabras claves:Anti-diabetic agents, FURIA-C, induction rule, Lda, machine-learning techniques, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, González‐díaz H., Karel Diéguez-Santana, Rasulev B., Rivera-Borroto O.M.Fuentes:googlescopusA new topological descriptors based model for predicting intestinal epithelial transport of drugs in caco-2 cell culture
ArticleAbstract: Purpose: Quantitative Structure-Permeability Relationships (QSPerR) of the intestinal permeability aPalabras claves:Autores:González‐díaz H., Pérez M.A.C., Romero-Zaldivar V., Torrens F., Yovani Marrero-PonceFuentes:googlescopusComputational chemistry comparison of stable/nonstable protein mutants classification models based on 3d and topological indices
ArticleAbstract: In principle, there are different protein structural parameters that can be used in computational chPalabras claves:Protein stability, Protein structure, Spectral moments, topological indicesAutores:González‐díaz H., Podda G., Uriarte E., Yunierkis Perez-CastilloFuentes:googlescopusGene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis
ArticleAbstract: Consensus strategy was proved to be highly efficient in the recognition of gene-disease association.Palabras claves:Autores:Alejandro Cabrera-Andrade, Andrés López-Cortés, Cesar Paz-y-Miño, Eduardo Tejera, González‐díaz H., Munteanu C.R., Sierra A.P., Stephen Jones Barigye, Yunierkis Perez-CastilloFuentes:googlescopusHP-Lattice QSAR for dynein proteins: Experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence
ArticleAbstract: The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projectsPalabras claves:2D-electrophoresis, BLAST, complex networks, Dyneins, HP-Lattice model, Leishmania infantum, Mass spectrometry, Parasites proteomics, QSAR, Sequence alignment, Spectral momentsAutores:Bolas-Fernández F., Chou K.C., Dea-Ayuela M.A., González‐díaz H., Meneses-Marcel A., Ubeira F.M., Yunierkis Perez-CastilloFuentes:googlescopusMMM-QSAR recognition of ribonucleases without alignment: Comparison with an HMM model and isolation from Schizosaccharomyces pombe, prediction, and experimental assay of a new sequence
ArticleAbstract: The study of type III RNases constitutes an important area in molecular biology. It is known that thPalabras claves:Autores:Agüero-Chapin G., Aminael Sánchez-Rodríguez, de la Riva G.A., González‐díaz H., Podda G., Rodríguez E., Vazquez-Padron R.I.Fuentes:googlescopusMachine 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:googlescopusMachine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends
ArticleAbstract: Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unPalabras claves:Antibacterial agents, Antibiotic Resistance, Bibliometric Analysis, Computer model in drug design, Machine learning, Network AnalysisAutores:González‐díaz H., Karel Diéguez-SantanaFuentes: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:googlescopusPredicting 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