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• W2116747618 abstract "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 representationsatom counts (2D) and radial distribution function (RDF) (3D)and two supervised classification methodscounterpropagation 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 approachesprincipal component analysis combined with Hotelling T2 statistic and an a posteriori probability-based rule." @default.
• W2116747618 created "2016-06-24" @default.
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• W2116747618 date "2006-12-02" @default.
• W2116747618 modified "2023-10-06" @default.
• W2116747618 title "Sesquiterpene Lactones-Based Classification of the Family Asteraceae Using Neural Networks and <i>k</i>-Nearest Neighbors" @default.
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• W2116747618 doi "https://doi.org/10.1021/ci060046x" @default.
• W2116747618 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17238243" @default.
• W2116747618 hasPublicationYear "2006" @default.
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