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Chemometrics for QSAR with low sequence homology: Mycobacterial promoter sequences recognition with 2D-RNA entropies
ArticleAbstract: Predicting mycobacterial sequences promoter of protein synthesis is important in the study of proteiPalabras claves:entropy, information theory, Machine learning algorithms, Markov models, Mycobacterial promoter sequences, QSAR, RNA secondary structureAutores:González-Díaz Y., González‐díaz H., Maykel Cruz-Monteagudo, Pérez-Bello A., Santana L., Uriarte E.Fuentes:scopusComplex 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:scopusComputer-aided nanotoxicology: Assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach
ArticleAbstract: Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalyPalabras claves:Autores:Alejandro Speck-Planche, González‐díaz H., Kleandrova V.V., Luan F., Melo A., Natalia Dias Soeiro Cordeiro M., Ruso J.M.Fuentes:scopusMulti-output model with Box–Jenkins operators of linear indices to predict 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:scopusNet-Net Auto Machine Learning (AutoML) Prediction 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:scopusNucleic acid quadratic indices of the "macromolecular graph's nucleotides adjacency matrix". Modeling of footprints after the interaction of paromomycin with the HIV-1 Ψ-RNA packaging region
ArticleAbstract: This report describes a new set of macromolecular descriptors of relevance to nucleic acid QSAR/QSPRPalabras claves:Footprinting, Nucleic Acid Quadratic Index, paromomycin, QSPR/QSAR, RNA HIV-1, TOMOCOMD-CANAR approachAutores:Castro E.A., De Armas R.R., González‐díaz H., Nodarse D., Torrens F., Yovani Marrero-Ponce, Zaldivar V.R.Fuentes:googlescopusUnified drug-target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects
ArticleAbstract: Most of present molecular descriptors consider just the molecular structure. In the present articlePalabras claves:ADL, Drugs side effects, Markov Model, QSAR, QSTR, thermodynamicAutores:González‐díaz H., Maykel Cruz-MonteagudoFuentes:scopusQSAR, complex networks, principal components and partial order analysis of drug cardiotoxicity with proteome mass-spectra topological indices
Book PartAbstract: Blood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac ToPalabras claves:Clinical Proteomics, complex networks, Markov Model, Mass spectrometry, Partial Order, Quantitative Structure-Property RelationshipAutores:Borges F., Concud R., González‐díaz H., Maykel Cruz-Monteagudo, Munteanu C.R., Natalia Dias Soeiro Cordeiro M.Fuentes:scopusMMM-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 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:googlescopus