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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.

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 pentose phosphate path (m = 2) for Arabidopsis under oxidative stress. The numbers in brackets represent the probability of each edge θm for m = 2.
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Figure 10: Transition probabilities for the most expressed pentose phosphate path (m = 2) for Arabidopsis under oxidative stress. The numbers in brackets represent the probability of each edge θm for m = 2.

Mentions: To visualize the oxidative class pathway we overlay the transition probabilities onto the pentose phosphate network (Figure 4) and clearly see the oxidative branch from C00668 to C00117 (D-Ribose-5P) is highlighted (Figure 10). The transition probabilities estimated by HME3M confirm the observations of [14] and show that when Arabidopsis is under oxidative stress the pentose phostphate pathway is clearly coordinated to produce D-Ribose-5P. However we observe that no single gene transitions can define the pathway but a coordinated set of genes that determine the path taken when the pentose phosphate cycle is subjected to oxidative stress.


A markov classification model for metabolic pathways.

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

Transition probabilities for the most expressed pentose phosphate path (m = 2) for Arabidopsis under oxidative stress. The numbers in brackets represent the probability of each edge θm for m = 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Transition probabilities for the most expressed pentose phosphate path (m = 2) for Arabidopsis under oxidative stress. The numbers in brackets represent the probability of each edge θm for m = 2.
Mentions: To visualize the oxidative class pathway we overlay the transition probabilities onto the pentose phosphate network (Figure 4) and clearly see the oxidative branch from C00668 to C00117 (D-Ribose-5P) is highlighted (Figure 10). The transition probabilities estimated by HME3M confirm the observations of [14] and show that when Arabidopsis is under oxidative stress the pentose phostphate pathway is clearly coordinated to produce D-Ribose-5P. However we observe that no single gene transitions can define the pathway but a coordinated set of genes that determine the path taken when the pentose phosphate cycle is subjected to oxidative stress.

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