File Entry: Identifying Metabolic Pathway within Microarray Gene Expression Data Using Combination of Probabilistic Models
Created: 2012-07-24 04:45:28
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| Title | Identifying Metabolic Pathway within Microarray Gene Expression Data Using Combination of Probabilistic Models |
| File name | hakim.pdf |
| File size | 326234 |
| SHA1 | f0ad10a1ddd1eea7be294b6269202056c3d6990d |
| Content type | Adobe PDF |
Description
Extracting metabolic pathway that dictates a specific biological
response is currently one of the important disciplines in metabolic system
biology research. Previous methods have successfully identified those pathways
but without concerning the genetic effect and relationship of the genes, the
underlying structure is not precisely represented and cannot be justified to be
significant biologically. In this article, probabilistic models capable of
identifying the significant pathways through metabolic networks that are related
to a specific biological response are implemented. This article utilized
combination of two probabilistic models, using ranking, clustering and
classification techniques to address limitations of previous methods with the
annotation to Kyoto Encyclopedia of Genes and Genomes (KEGG) to ensure
the pathways are biologically plausible.
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