Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computationally identifying transcription factor binding sites in the promoter regions of genes is an important problem in computational biology and has been under intensive research for a decade. To predict the binding site locations efficiently, many algorithms that incorporate either approximate or heuristic techniques have been developed. Hover, the prediction accuracy is not satisfactory and binding site prediction thus remains a challenging problem. ,  develop an approach that can be used to predict binding site motifs using a genetic algorithm. Based on the generic framework of a genetic algorithm, the approach explores the search space of all possible starting locations of the binding site motifs in different target sequences with a population that undergoes evolution. Individuals in the population compete to participate in the crossovers and mutations occur with a certain probability. Initial experiments demonstrated that our approach could achieve high prediction accuracy in a small amount of computation time. A promising advantage of our approach is the fact that the computation time does not explicitly depend on the length of target sequences and hence may not increase significantly when the target sequences become very long.
['Dongsheng Che', 'Yinglei Song', 'Khaled Rasheed']
I am writing a research paper on a idea MDGA: motif discovery using a genetic algorithm
[{"from": "human", "value": "I am writing a research paper on a idea MDGA: motif discovery using a genetic algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computationally identifying transcription factor binding sites in the promoter regions of genes is an important problem in computational biology and has been under intensive research for a decade. To predict the binding site locations efficiently, many algorithms that incorporate either approximate or heuristic techniques have been developed. Hover, the prediction accuracy is not satisfactory and binding site prediction thus remains a challenging problem. ,  develop an approach that can be used to predict binding site motifs using a genetic algorithm. Based on the generic framework of a genetic algorithm, the approach explores the search space of all possible starting locations of the binding site motifs in different target sequences with a population that undergoes evolution. Individuals in the population compete to participate in the crossovers and mutations occur with a certain probability. Initial experiments demonstrated that our approach could achieve high prediction accuracy in a small amount of computation time. A promising advantage of our approach is the fact that the computation time does not explicitly depend on the length of target sequences and hence may not increase significantly when the target sequences become very long."}]