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How to find simple and accurate rules for viral protease cleavage specificities.

Rögnvaldsson T, Etchells TA, You L, Garwicz D, Jarman I, Lisboa PJ - BMC Bioinformatics (2009)

Bottom Line: However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate.The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease.A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users.

View Article: PubMed Central - HTML - PubMed

Affiliation: Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se

ABSTRACT

Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.

Results: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.

Conclusion: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

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The receiver operator characteristic (ROC) for the OSRE rules’ accuracy on the out-of-sample HIV-1 PR 3261 data set. The triangles show the results for the OSRE HIV-1 PR 746 rules and the circles show the results for the OSRE HIV-1 PR 1625 rules (the five symbols correspond to results using 1, 2, 3, 4, and 5 rules, respectively). The diamonds and stars show the rough set rules’ prediction accuracy, Table III and Fig. 1b in [17]. The diamonds and stars show the results when 1, 2, 3, ..., and 9 rules are used from [17]. The squares and the crosses in the detail Figure, Fig. 4, are the results for Table IV and Fig. 1a in [17]. The dots show the prediction accuracy for the HIVcleave web-server [37]. The solid curve shows the ROC curve for the hitherto best predictor, a linear support vector machine trained on the HIV-1 PR 1625 data set [38], which does not provide any rules. The dashed diagonal line marks the expected results for random prediction.
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Figure 3: The receiver operator characteristic (ROC) for the OSRE rules’ accuracy on the out-of-sample HIV-1 PR 3261 data set. The triangles show the results for the OSRE HIV-1 PR 746 rules and the circles show the results for the OSRE HIV-1 PR 1625 rules (the five symbols correspond to results using 1, 2, 3, 4, and 5 rules, respectively). The diamonds and stars show the rough set rules’ prediction accuracy, Table III and Fig. 1b in [17]. The diamonds and stars show the results when 1, 2, 3, ..., and 9 rules are used from [17]. The squares and the crosses in the detail Figure, Fig. 4, are the results for Table IV and Fig. 1a in [17]. The dots show the prediction accuracy for the HIVcleave web-server [37]. The solid curve shows the ROC curve for the hitherto best predictor, a linear support vector machine trained on the HIV-1 PR 1625 data set [38], which does not provide any rules. The dashed diagonal line marks the expected results for random prediction.

Mentions: The sensitivity and specificity are usually shown together in a so-called receiver operator characteristic (ROC) curve. The ROC plots for the OSRE HIV-1 rules are shown in Figure 3 and Figure 4, together with the same values for the rough set theory rules [17], the recently published HIVcleave web-server [37], and a linear support vector machine [38], which was the best predictor we had hitherto tried for this problem. The positive likelihood ratio is shown in Table 5, together with corresponding sensitivity and specificity values.


How to find simple and accurate rules for viral protease cleavage specificities.

Rögnvaldsson T, Etchells TA, You L, Garwicz D, Jarman I, Lisboa PJ - BMC Bioinformatics (2009)

The receiver operator characteristic (ROC) for the OSRE rules’ accuracy on the out-of-sample HIV-1 PR 3261 data set. The triangles show the results for the OSRE HIV-1 PR 746 rules and the circles show the results for the OSRE HIV-1 PR 1625 rules (the five symbols correspond to results using 1, 2, 3, 4, and 5 rules, respectively). The diamonds and stars show the rough set rules’ prediction accuracy, Table III and Fig. 1b in [17]. The diamonds and stars show the results when 1, 2, 3, ..., and 9 rules are used from [17]. The squares and the crosses in the detail Figure, Fig. 4, are the results for Table IV and Fig. 1a in [17]. The dots show the prediction accuracy for the HIVcleave web-server [37]. The solid curve shows the ROC curve for the hitherto best predictor, a linear support vector machine trained on the HIV-1 PR 1625 data set [38], which does not provide any rules. The dashed diagonal line marks the expected results for random prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2698905&req=5

Figure 3: The receiver operator characteristic (ROC) for the OSRE rules’ accuracy on the out-of-sample HIV-1 PR 3261 data set. The triangles show the results for the OSRE HIV-1 PR 746 rules and the circles show the results for the OSRE HIV-1 PR 1625 rules (the five symbols correspond to results using 1, 2, 3, 4, and 5 rules, respectively). The diamonds and stars show the rough set rules’ prediction accuracy, Table III and Fig. 1b in [17]. The diamonds and stars show the results when 1, 2, 3, ..., and 9 rules are used from [17]. The squares and the crosses in the detail Figure, Fig. 4, are the results for Table IV and Fig. 1a in [17]. The dots show the prediction accuracy for the HIVcleave web-server [37]. The solid curve shows the ROC curve for the hitherto best predictor, a linear support vector machine trained on the HIV-1 PR 1625 data set [38], which does not provide any rules. The dashed diagonal line marks the expected results for random prediction.
Mentions: The sensitivity and specificity are usually shown together in a so-called receiver operator characteristic (ROC) curve. The ROC plots for the OSRE HIV-1 rules are shown in Figure 3 and Figure 4, together with the same values for the rough set theory rules [17], the recently published HIVcleave web-server [37], and a linear support vector machine [38], which was the best predictor we had hitherto tried for this problem. The positive likelihood ratio is shown in Table 5, together with corresponding sensitivity and specificity values.

Bottom Line: However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate.The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease.A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users.

View Article: PubMed Central - HTML - PubMed

Affiliation: Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se

ABSTRACT

Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.

Results: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods.

Conclusion: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

Show MeSH
Related in: MedlinePlus