J Chem Inf Model - Classifying molecules using a sparse probabilistic kernel binary classifier.

Tópicos

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{ method(1219) similar(1157) match(930) }
{ compound(1573) activ(1297) structur(1058) }
{ model(2656) set(1616) predict(1553) }
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{ decis(3086) make(1611) patient(1517) }
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{ gene(2352) biolog(1181) express(1162) }
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Resumo

The central idea of supervised classification in chemoinformatics is to design a classifying algorithm that accurately assigns a new molecule to one of a set of predefined classes. Tipping has devised a classifying scheme, the Relevance Vector Machine (RVM), which is in terms of sparsity equivalent to the Support Vector Machine (SVM). However, unlike SVM classifiers, the RVM classifiers are probabilistic in nature, which is crucial in the field of decision making and risk taking. In this work, we investigate the performance of RVM binary classifiers on classifying a subset of the MDDR data set, a standard molecular benchmark data set, into active and inactive compounds. Additionally, we present results that compare the performance of SVM and RVM binary classifiers.

Resumo Limpo

central idea supervis classif chemoinformat design classifi algorithm accur assign new molecul one set predefin class tip devis classifi scheme relev vector machin rvm term sparsiti equival support vector machin svm howev unlik svm classifi rvm classifi probabilist natur crucial field decis make risk take work investig perform rvm binari classifi classifi subset mddr data set standard molecular benchmark data set activ inact compound addit present result compar perform svm rvm binari classifi

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