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Artur M. S. Silva
Researcher at University of Aveiro
Publications - 961
Citations - 19708
Artur M. S. Silva is an academic researcher from University of Aveiro. The author has contributed to research in topics: Catalysis & Chemistry. The author has an hindex of 54, co-authored 895 publications receiving 15627 citations. Previous affiliations of Artur M. S. Silva include University of Porto & University of Santiago de Compostela.
Papers
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Journal ArticleDOI
How can artificial intelligence be used for peptidomics
Luís Perpétuo,Julie Klein,Rita Ferreira,Sofia Guedes,Francisco Amado,Adelino F. Leite-Moreira,Artur M. S. Silva,Visith Thongboonkerd,Rui Vitorino,Rui Vitorino +9 more
TL;DR: The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach as discussed by the authors, which can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science.
Cytisus multiflorus: source of antioxidant polyphenols
Olívia R. Pereira,Maria J. Perez,Rócio I.R. Macias,Maria Rosário Domingues,Artur M. S. Silva,Jose J.G. Marín,Susana M. Cardoso +6 more
TL;DR: In this paper, the phenolic composition and the antioxidant capacity of an ethanolic extract from flowers of C. multiflorus were investigated by HPLC-DAD, ESI-MS and NMR combined analysis.
Cyclic voltammetric analysis of 2-styrylchromones
Journal ArticleDOI
A Novel Short-Step Synthesis of New Xanthenedione Derivatives from the Cyclization of 3-Cinnamoyl-2-styrylchromones
Diana C. G. A. Pinto,Ana M. L. Seca,Stephanie B. Leal,Artur M. S. Silva,José A. S. Cavaleiro +4 more
TL;DR: In this article, the authors proposed the Baker-Venkataraman rearrangement of the bisarylacrylate (I) under optimized conditions A) produces the desired 2-styrylchromone (II).
Book ChapterDOI
Predicting Research and Motor Octane Numbers based on Near Infrared Spectroscopy: Models based on Partial Least Squares Regression and Artificial Neural Networks
TL;DR: In this paper, the performance of two different multivariate models, based on partial least squares regression and artificial networks, was compared for the prediction of research and motor octane numbers of gasolines.