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Pathway based similarity measuring method shows higher classification accuracy.
We showed pathway expression profiling based similarity measuring method outperformed conventional gene expression profile based similarity measuring method over at most 50% for cross-platform profile search and 10% for cross-experiment profile search.
As previously developed pathway based gene expression analysis methods were successfully improve intact gene expression based analysis methods [ 20- 23], pathway based similarity measuring method outperformed conventional method.
Pathway based similarity measuring method, PEPC, consistently shows higher classification accuracies than CGSEP method for classifications with three different search databases.
To evaluate the performance of pathway based similarity measuring method, we conducted two more cell type classifications, cross-platform and cross-experiment classification, where search space is consist of profiles whose platforms or experiments are different from that of query.
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The pathway based gene expression similarity measuring method outperforms commonly used similarity measuring methods.
For this purpose, we devised a pathway based gene expression similarity measuring method.
To evaluate pathway based gene expression similarity measuring method, we conducted cell type classification test.
Considering the fact that public microarray database is consist of gene expression profiles of various experiments with various type of platform, pathway based gene expression similarity measuring method could be successfully applied for searching large public microarray databases.
We devised new gene expression similarity measuring method based on pathway information.
The pathway based gene expression similarity measuring methods, PEPC, we suggested here, compute pathway level expression by averaging expression of genes mapped to pathway.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com