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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Bibliographic Details
Published in:Journal of computer-aided molecular design Vol. 21; no. 5; pp. 269 - 280
Main Authors: Cannon, Edward O., Amini, Ata, Bender, Andreas, Sternberg, Michael J. E., Muggleton, Stephen H., Glen, Robert C., Mitchell, John B. O.
Format: Journal Article
Language:English
Published: Netherlands Springer Nature B.V 01.05.2007
Subjects:
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
Online Access:Get full text
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Summary: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 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.
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ISSN:0920-654X
1573-4951
DOI:10.1007/s10822-007-9113-3