Natural sesquiterpenoids

Fraga, Braulio M.

doi:10.1039/np9900700061

Cited by 14 publications

(2 citation statements)

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“…Another possible reason for misclassification with any model might be that the new instances, which are to be tested, lie far away from the prediction space. Although the models presented herein are aimed at a specific class of secondary metabolites, i.e., STLs, the problem is still present since several novel STLs are reported each year, − some of them with rather unusual structures, including for example dimers . This was what we examined in turn.…”

Section: Discussionmentioning

confidence: 98%

“…Data Sets. The structures of the 921 STLs and the information concerning their taxonomic origin were taken from comprehensive surveys published in the literature. ,− The taxa (tribes and genera) presented herein are in accordance to the division made by Bremer . This information was used to assign all chemical structures to their corresponding taxa.…”

Section: Methodsmentioning

confidence: 99%

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Sesquiterpene Lactones-Based Classification of the Family Asteraceae Using Neural Networks and k-Nearest Neighbors

Hristozov

Costa

Gasteiger

2006

J. Chem. Inf. Model.

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In a recent publication we described the application of an unsupervised learning method using self-organizing maps to the separation of three tribes and seven subtribes of the plant family Asteraceae based on a set of sesquiterpene lactones (STLs) isolated from individual species. In the present work, two different structure representations--atom counts (2D) and radial distribution function (RDF) (3D)--and two supervised classification methods--counterpropagation neural networks and k-nearest neighbors (k-NN)--were used to predict the tribe in which a given STL occurs. The data set was extended from 144 to 921 STLs, and the Asteraceae tribes were augmented from three to seven. The k-NN classifier with k = 1 showed the best performance, while the RDF code outperformed the atom counts. The quality of the obtained model was assessed with two test sets, which exemplified two possible applications: (1) finding a plant source for a desired compound and (2) based on a plant species chemical profile (STLs): (a) study the relationship between the current taxonomic classification and plant's chemistry and (b) assign a species to a tribe by majority vote. In addition, the problem of defining the applicability domain of the models was assessed by means of two different approaches-principal component analysis combined with Hotelling T2 statistic and an a posteriori probability-based rule.

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