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A markov classification model for metabolic pathways.

Hancock T, Mamitsuka H - Algorithms Mol Biol (2010)

Bottom Line: The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise.This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways.HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

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Affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan. timhancock@kuicr.kyoto-u.ac.jp

ABSTRACT

Background: This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.

Results: We compared the performance of HME3M with logistic regression and support vector machines (SVM) for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for Arabidopsis thaliana. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of Arabidopsis. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of Arabidopsis.

Conclusions: This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

No MeSH data available.


Related in: MedlinePlus

Performance results for the Glycolysis pathway. Inverse cross-validated Correct Classification Rates (CCR) for all models for the Glycolysis pathway for Arabidopsis.
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Figure 5: Performance results for the Glycolysis pathway. Inverse cross-validated Correct Classification Rates (CCR) for all models for the Glycolysis pathway for Arabidopsis.

Mentions: The glycolysis experiment results are displayed in Figure 5. Figure 5 presents the mean correct classification rates (CCR) for HME3M and comparison methods for each pathway dataset built from the three trailed gene activity tolerances. The number of mixture components M is varied from 2 to 10. It is clear from Figure 5 that for all tolerances the mean CCR for HME3M after M = 2 is consistently greater than all other methods and the optimal performance being observed at M = 4. An interesting feature of Figure 5 is that after the optimal performance has been reached, the addition of more components seems to not affect the overall classification accuracy. This shows HME3M to be resistant to overfitting and complements the results of the noise simulation experiments in Table 3.


A markov classification model for metabolic pathways.

Hancock T, Mamitsuka H - Algorithms Mol Biol (2010)

Performance results for the Glycolysis pathway. Inverse cross-validated Correct Classification Rates (CCR) for all models for the Glycolysis pathway for Arabidopsis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2823754&req=5

Figure 5: Performance results for the Glycolysis pathway. Inverse cross-validated Correct Classification Rates (CCR) for all models for the Glycolysis pathway for Arabidopsis.
Mentions: The glycolysis experiment results are displayed in Figure 5. Figure 5 presents the mean correct classification rates (CCR) for HME3M and comparison methods for each pathway dataset built from the three trailed gene activity tolerances. The number of mixture components M is varied from 2 to 10. It is clear from Figure 5 that for all tolerances the mean CCR for HME3M after M = 2 is consistently greater than all other methods and the optimal performance being observed at M = 4. An interesting feature of Figure 5 is that after the optimal performance has been reached, the addition of more components seems to not affect the overall classification accuracy. This shows HME3M to be resistant to overfitting and complements the results of the noise simulation experiments in Table 3.

Bottom Line: The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise.This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways.HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan. timhancock@kuicr.kyoto-u.ac.jp

ABSTRACT

Background: This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.

Results: We compared the performance of HME3M with logistic regression and support vector machines (SVM) for both simulated pathways and on two metabolic networks, glycolysis and the pentose phosphate pathway for Arabidopsis thaliana. We use AltGenExpress microarray data and focus on the pathway differences in the developmental stages and stress responses of Arabidopsis. The results clearly show that HME3M outperformed the comparison methods in the presence of increasing network complexity and pathway noise. Furthermore an analysis of the paths identified by HME3M for each metabolic network confirmed known biological responses of Arabidopsis.

Conclusions: This paper clearly shows HME3M to be an accurate and robust method for classifying metabolic pathways. HME3M is shown to outperform all comparison methods and further is capable of identifying known biologically active pathways within microarray data.

No MeSH data available.


Related in: MedlinePlus