We identified small size cancer-specific gene regulatory network (colon cancer & prostate cancer) from microarray data using computational intelligence. Now we would like to validate it with experimental results. We are looking for regulation databases having details about interactions among genes in a particular cancer. We also like to perform Gene Ontology analysis.
Kindly suggest cancer regulation databases and how to perform GO analysis.
I think it really depends on what you have.
If you have enough time, facilities and cases for the cancers then wet laboratory experiments and clinical trials will be the best way to validate GRN.
If you can accept in silico methods, then network-based approaches by evaluating their associations with other networks could be the one way to validate your GRN.
However, in silico methods are still considered to be unmatured yet, so their specificity and validations toward specific diseases are still needed to be studied further.
You can get more detailed information about network approaches via following article
http://www.nature.com/nrg/journal/v12/n1/full/nrg2918.html
For GO analysis you can refer following article and application
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6732457
http://www.ittc.ku.edu/chenlab/goal/
If you want to know more about GOAL or BSM score, you need to wait little more time for reading official version of our journal paper which will be published next month.
J. C. Jeong, and X. W. Chen, "A New Semantic Functional Similarity over Gene Ontology," IEEE/ACM Trans Comput Biol Bioinform, 2014.
http://bib.oxfordjournals.org/content/12/3/245.abstract. Please see this article. validation of an entire GRN requires lot of time and error proof methods to be successful. You can check out articles by Barabazi and team. For performing GO analysis you can obtain the gene names or gene ids of the ones in your network and add it to GO databases. You can use EASE which is a simple software for GO analysis. If you are planning to validate your GRN then it will be good to start with modules on the network and then try by expanding them according to the availability of your time and cost.
One of the most simple way is to map you data into KEGG pathway. You can map your list of genes into corresponding KEGG cancer pathway using user data mapping tool supplied by KEGG pathway.
If you found some interesting candidate genes, then you can do further study on these genes.
Also you can search your genes against CGC, OMIM, or HGMD to find interesting candidates.
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