Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks
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Data
2004-11-01
Autores
Ferreira, MJP
Brant, AJC
Rufino, A. R.
Alvarenga, SAV
Magri, FMM
Emerenciano, V. P.
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Editor
Wiley-Blackwell
Resumo
The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%. Copyright (C) 2004 John Wiley Sons, Ltd.
Descrição
Palavras-chave
artificial neural networks, chemical composition, occurrence number, secondary metabolites, Asteraceae
Como citar
Phytochemical Analysis. Chichester: John Wiley & Sons Ltd, v. 15, n. 6, p. 389-396, 2004.