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Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome
ArticleAbstract: The spatial distribution of genes in chromosomes seems not to be random. For instance, only 10% of gPalabras claves:chromosome microstructure, complex networks, gene orientation, Machine learning, MALARIA, Plasmodium sp. proteomeAutores:Bernabé Ortega-Tenezaca, González‐díaz H., Quevedo‐tumailli V.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:scopusA multi-objective approach for anti-osteosarcoma cancer agents discovery through drug repurposing
ArticleAbstract: Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year surviPalabras claves:Drug repositioning, Machine learning, Multi-objective model, Osteosarcoma, Virtual ScreeningAutores:Alejandro Cabrera-Andrade, Andrés López-Cortés, Eduardo Tejera, Gabriela Fernanda Jaramillo-Koupermann, González‐díaz H., Munteanu C.R., Sierra A.P., Yunierkis Perez-CastilloFuentes: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: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 pbkp_redict the activity of aPalabras claves:Antibacterial agents, Antibiotic Resistance, Bibliometric Analysis, Computer model in drug design, Machine learning, Network AnalysisAutores:González‐díaz H., Karel Diéguez-SantanaFuentes:googlescopusIfptml mapping of drug graphs with protein and chromosome structural networks vs. Pre‐clinical assay information for discovery of antimalarial compounds
ArticleAbstract: The parasite species of genus Plasmodium causes Malaria, which remains a major global health problemPalabras claves:Antimalarial compounds, CHEMBL, complex networks, Machine learning, NCBI‐GDV, perturbation theory, Plasmodium proteome, UniProtAutores:Bernabé Ortega-Tenezaca, González‐díaz H., Quevedo‐tumailli V.Fuentes:scopusPbkp_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:googlescopusPbkp_rediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches
ArticleAbstract: Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesPalabras claves:Aquatic toxicity, Machine learning, multiple linear regression, Quantitative Structure–Toxicity relationship, random forestAutores:Amilkar Puris, González‐díaz H., Karel Diéguez-Santana, Nachimba-Mayanchi M.M., Roldán Torres GutiérrezFuentes:googlescopusPtml multi-label algorithms: Models, software, and applications
ReviewAbstract: By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possi-ble to developPalabras claves:Cheminformatics, Drug Discovery, Large data sets, Machine learning, Multi-target models, perturbation theory, PTMLAutores:Arrasate S., Bediaga H., Bernabé Ortega-Tenezaca, Collados J., González‐díaz H., Madariaga G., Munteanu C.R., Natalia Dias Soeiro Cordeiro M., Quevedo‐tumailli V.Fuentes:scopus