Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Prediction of promoter regions continues to be a challenging subproblem in mapping out eukaryotic DNA. While this task is key to understanding the regulation of differential transcription, the gene-specific architecture of promoter sequences does not readily lend itself to general strategies. To date, the best approaches are based on Support Vector Machines (SVMs) that employ standard ”spectrum” features and achieve promoter region classification accuracies from a low of 84% to a high of 94% depending on the particular species involved. ,  propose a general and porful methodology that uses Genetic Programming (GP) techniques to generate more complex and more gene-specific features to be used with a standard SVM for promoter region identification.  evaluate our methodology on three data sets from different species and observe consistent classification accuracies in the 94–95% range. In addition, because the GP-generated features are gene-specific, they can be used by biologists to advance their understanding of the architecture of eukaryotic promoter regions.
['Uday Kamath', 'Kenneth A. De Jong', 'Amarda Shehu']
I am writing a research paper on a idea An evolutionary-based approach for feature generation: Eukaryotic promoter recognition
[{"from": "human", "value": "I am writing a research paper on a idea An evolutionary-based approach for feature generation: Eukaryotic promoter recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Prediction of promoter regions continues to be a challenging subproblem in mapping out eukaryotic DNA. While this task is key to understanding the regulation of differential transcription, the gene-specific architecture of promoter sequences does not readily lend itself to general strategies. To date, the best approaches are based on Support Vector Machines (SVMs) that employ standard \u201dspectrum\u201d features and achieve promoter region classification accuracies from a low of 84% to a high of 94% depending on the particular species involved. ,  propose a general and porful methodology that uses Genetic Programming (GP) techniques to generate more complex and more gene-specific features to be used with a standard SVM for promoter region identification.  evaluate our methodology on three data sets from different species and observe consistent classification accuracies in the 94\u201395% range. In addition, because the GP-generated features are gene-specific, they can be used by biologists to advance their understanding of the architecture of eukaryotic promoter regions."}]