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Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies
ArticleAbstract: In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and αPalabras claves:antidiabetic agents, Decision Trees, linear discriminant analysis, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Pham-The H., Rasulev B., Rivera-Borroto O.M., Thu H.L.T.Fuentes:scopusA 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:googlescopusCarbon 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:scopusExploring proteasome inhibition using atomic weighted vector indices and machine learning approaches
ArticleAbstract: Ubiquitin–proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradatiPalabras claves:AWV, deep learning, Descriptor, GA, ML, UpsAutores:Garit J., Gerardo M. Casañola-Martin, Oscar Martínez Santiago, Rasulev B., Rodríguez-González A.Y., Stephen Jones Barigye, Yoan Martínez LópezFuentes: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: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:googlescopusThe Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data
ArticleAbstract: Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. NoPalabras claves:Isoenzymes, Molecular docking, molecular dynamics, molecular modeling, PON1, QM/MM, QSAR, RPON1Autores:Gauld J.W., Gerardo M. Casañola-Martin, Karabulut S., Mansour B., Rasulev B.Fuentes:scopus