Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. ,  verify the aforementioned fact, but  also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature.  proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions,  derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution.
['Michalis E. Blazadonakis', 'Michalis Zervakis', 'Dimitris Kafetzopoulos']
I am writing a research paper on a idea Complementary Gene Signature Integration in Multiplatform Microarray Experiments
[{"from": "human", "value": "I am writing a research paper on a idea Complementary Gene Signature Integration in Multiplatform Microarray Experiments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. ,  verify the aforementioned fact, but  also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature.  proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions,  derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution."}]