Send to

Choose Destination
Bioinformatics. 2008 Jan 15;24(2):250-7. Epub 2007 Nov 24.

Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis.

Author information

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.



Pathway knowledge in public databases enables us to examine how individual metabolites are connected via chemical reactions and what genes are implicated in those processes. For two given (sets of) compounds, the number of possible paths between them in a metabolic network can be intractably large. It would be informative to rank these paths in order to differentiate between them.


Focusing on adjacent pairwise coexpression, we developed an algorithm which, for a specified k, efficiently outputs the top k paths based on a probabilistic scoring mechanism, using a given metabolic network and microarray datasets. Our idea of using adjacent pairwise coexpression is supported by recent studies that local coregulation is predominant in metabolism. We first evaluated this idea by examining to what extent highly correlated gene pairs are adjacent and how often they are consecutive in a metabolic network. We then applied our algorithm to two examples of path ranking: the paths from glucose to pyruvate in the entire metabolic network of yeast and the paths from phenylalanine to sinapyl alcohol in monolignols pathways of arabidopsis under several different microarray conditions, to confirm and discuss the performance analysis of our method.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center