Finally, we applied NetGenerator to microarray time series data gained from human mesenchymal stem cells.
One aspect in the context of the analysis of microarray data that gained recently widespread interest is the inference of causal interactions among hundreds or thousands of genes [ 15- 22].
For example, the main mechanism may be mediated by a regulatory RNA, which may be neither represented in the network, nor in the expression data gained by microarray experiments.
In view of conserved cellular aspects of spermatogenesis, we anticipate that most genes will show conservation of their expression patterns at subsequent steps of spermatogenesis, between mouse and primates, and we used the available mouse microarray data to gain information about spermatogenic gene expression patterns in whole chimpanzee and human testes.
We have presented a method that allows the use of publicly available microarray data to gain insight into human biology and disease.
Next, microarray data from gain-of-function (GOF) analysis obtained after electroporation of either Ngn2 or Mash1 in the developing mouse dorsal or ventral telencephalon, respectively, were fused with previous and new microarray results from proneural loss-of-function (LOF) experiments [ 4].
These results, taken together, suggest that inflammatory processes and resultant tissue remodeling partly explain genome-wide expression patterns associated with HF diet, that microarray data can be exploited to gain biological insights into these patterns, and that the intensity of hepatic inflammation in response to HF diet is genetically regulated and heterogeneous among inbred mouse strains.
For both patients and microarray data sets genes keeping or gaining H3K4me3 in tumors were significantly over-represented among up-regulated genes and significantly under-represented among down-regulated genes.
Of the 27 medulloblastomas with both CGH and microarray data, 11% displayed 8q gain, whose expression profiles were compared to the other 89% of tumors.
Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis.
In summary, we have developed a new method to combine microarray data with a constraint-based formalism to gain deeper understanding of the system-level metabolic behavior of cells following a wide range of perturbations.
