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Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach
ArticleAbstract: The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a laPalabras claves:Autores:Aguilera-Mendoza L., Beltran J.A., Brizuela C.A., César R. García-Jacas, Chavez E., Guillen-Ramirez H.A., Yovani Marrero-PonceFuentes:scopusEnsemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Pbkp_rediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches
ArticleAbstract: Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatoPalabras claves:Autores:César R. García-Jacas, Jose R. Mora, Suarez Causado A., Yovani Marrero-PonceFuentes:scopusDo deep learning models make a difference in the identification of antimicrobial peptides?
ArticleAbstract: In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classPalabras claves:Autores:Brizuela C.A., César R. García-Jacas, García-González L.A., Pinacho-Castellanos S.A.Fuentes:scopusHandcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: Complementary or redundant?
ArticleAbstract: Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to becomPalabras claves:Antimicrobial peptides, deep learning, Explainable artificial intelligence, handcrafted features, Non-handcrafted features, self-supervision, shallow learningAutores:Brizuela C.A., César R. García-Jacas, García-González L.A., Martinez-Rios F.O., Tapia-Contreras I.P.Fuentes:scopus