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When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data,(More)
BACKGROUND Predicting the effects of genetic modification is difficult due to the complexity of metabolic net- works. Various gene knockout strategies have been utilised to deactivate specific genes in order to determine the effects of these genes on the function of microbes. Deactivation of genes can lead to deletion of certain proteins and functions.(More)
Advancements in pathway-based microarray classification approach leads to a new era of genomic research. However, this approach is limited by issues regarding the quality of the pathway data as these data are usually curated from biological literatures and in specific biological experiment (e.g. lung cancer experiment), context free pathway information(More)
Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data.(More)
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