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- W2040226901 endingPage "396" @default.
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- W2040226901 abstract "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%." @default.
- W2040226901 created "2016-06-24" @default.
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- W2040226901 date "2004-01-01" @default.
- W2040226901 modified "2023-10-17" @default.
- W2040226901 title "Prediction of occurrences of diverse chemical classes in the Asteraceae through arti?cial neural networks" @default.
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- W2040226901 doi "https://doi.org/10.1002/pca.799" @default.
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