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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:googlescopusN-linear algebraic maps for chemical structure codification: A suitable generalization for atom-pair approaches?
ArticleAbstract: The present manuscript introduces, for the first time, a novel 3D-QSAR alignment free method (QuBiLSPalabras claves:3D Three-linear and four-linear indices, aggregation operator, Cramer's steroid, N-tuple simple stochastic and mutual probability matrices, N-tuple spatial-(Dis)similarity matrix, Principal Component Analysis, QSAR, QuBiLS-MIDAS N-tuples, Shannon entropy, ToMoCoMD-CARDD, Variability analysisAutores:César R. García-Jacas, Olivero-Verbel J., Rivera-Borroto O.M., Stephen Jones Barigye, Valdés-Martiní J.R., Yovani Marrero-PonceFuentes:scopus