Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 10...

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Vydáno v:	Journal of computer-aided molecular design Ročník 21; číslo 5; s. 269 - 280
Hlavní autoři:	Cannon, Edward O., Amini, Ata, Bender, Andreas, Sternberg, Michael J. E., Muggleton, Stephen H., Glen, Robert C., Mitchell, John B. O.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Springer Nature B.V 01.05.2007
Témata:	Bayes Theorem Chemical compounds Classification Classifiers Computational Biology Confidence Intervals Correlation coefficient Correlation coefficients Drug Design Logic programming Pharmaceutical Preparations - chemical synthesis Pharmaceutical Preparations - classification Pharmaceutical Preparations - metabolism Recall Software Support vector machines
ISSN:	0920-654X, 1573-4951
On-line přístup:	Získat plný text
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