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Cytochrome P450 Classification of Drugs with Support Vector Machines Implementing the Nearest Point Algorithm

Kless, Achim ; Eitrich, Tatjana

Knowledge Exploration in Life Science Informatics, p.191-205 [Periódico revisado por pares]

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Título:
Cytochrome P450 Classification of Drugs with Support Vector Machines Implementing the Nearest Point Algorithm
Autor: Kless, Achim ; Eitrich, Tatjana
Assuntos: Enrichment Factor ; Feature Selection ; Feature Selection Algorithm ; Support Vector Machine ; Support Vector Machine Algorithm
É parte de: Knowledge Exploration in Life Science Informatics, p.191-205
Descrição: Cytochrome P450s are an important class of drug metabolizing enzymes which play a significant role in drug metabolism, and thus in the drug discovery process. With a data set that was compiled from public available data on cytochrome P450 drug interaction data, and derived calculated chemoinformatics data, we have built binary classifiers based on kernel methods, in particular support vector machines implementing the nearest point algorithm. Feature selection is used as a preliminary stage of supervised learning. We work on supervised as well as on unsupervised selection methods. The classification results from a selected subset of the test set are compared structurally with compounds from the training set.
Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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