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The success of our method is mainly due to the integrated use of multiple genomics data.
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The iBBiG bi-clustering algorithm is optimized for module discovery in sparse noisy binary genomics data and can be used for meta-GSA of multiple genomics datasets, to discover modules: groups of phenotypes whose differential gene expression profiles are enriched in the same gene sets.
A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication.
While it was shown that multiple methods converge to provide a similar picture of the O157 H7 population structure, we are not advocating the routine use of multiple genotyping methods but rather the use of methods based on comparative genomics.
The use of multiple immunogens.
The importance of integrating multiple genomics information has been well recognized by the research community.
By use of comparative genomics techniques, experimental results can be transferred from one genome to another, while at the same time minimizing errors by requiring discovery in multiple genomes.
Kuwabara P, O'Neil N. The use of functional genomics in C. elegans for studying human development and disease.
There are still quite a few things holding back such broad use of personal genomics.
The use of comparative genomics may therefore become important to assist with the classification of gibbons.
Sole use of comparative genomics is possible.
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