Limits...
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.

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

Transition probabilities for the most expressed glycolysis path (m = 3) that separates flowers from rosette for Arabidopsis. Training set CCR = 0.818, AUC = 0.752.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2823754&req=5

Figure 7: Transition probabilities for the most expressed glycolysis path (m = 3) that separates flowers from rosette for Arabidopsis. Training set CCR = 0.818, AUC = 0.752.

Mentions: The ROC curves for each HME3M component are presented in Figure 6 and clearly show that the third component is the most important with an AUC of 0.752, whereas the other three components seem to hold limited or no predictive power. A bar plot of the HME3M transition probabilities (θm) for the third (m = 3) component is presented in Figure 7. Overlaying the transition probabilities from Figure 7 onto the full network in Figure 3 it is found that for three transitions only single genes are required for the reaction to proceed:


A markov classification model for metabolic pathways.

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

Transition probabilities for the most expressed glycolysis path (m = 3) that separates flowers from rosette for Arabidopsis. Training set CCR = 0.818, AUC = 0.752.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Transition probabilities for the most expressed glycolysis path (m = 3) that separates flowers from rosette for Arabidopsis. Training set CCR = 0.818, AUC = 0.752.
Mentions: The ROC curves for each HME3M component are presented in Figure 6 and clearly show that the third component is the most important with an AUC of 0.752, whereas the other three components seem to hold limited or no predictive power. A bar plot of the HME3M transition probabilities (θm) for the third (m = 3) component is presented in Figure 7. Overlaying the transition probabilities from Figure 7 onto the full network in Figure 3 it is found that for three transitions only single genes are required for the reaction to proceed:

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