Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a statistical framework, MAMS-M, for determining the methylation status of hundreds of cancer related CpG sites. MAMS-M extends and adapts our previous SNP genotyping algorithm, MAMS, to methylation bead array data, exploiting the similarities in data structure beten the two platforms. MAMS-M employs a multi-site, multi-array model-based clustering approach to derive initial methylation calls, and then recalibrate these calls and associated confidence measures using site-specific adjustments.  demonstrate the performance of MAMS-M using a real-life data set with cancer applications.
['Yuanyuan Xiao', 'Mark R. Segal', 'E. A. Houseman', 'Joseph L. Wiemels', 'John K. Wiencke', 'Shichun Zheng', 'Margaret Wrensch', 'Brock C. Christensen', 'Carmen J. Marsit', 'Karl T. Kelsey', 'Heather H. Nelson', 'Margaret R. Karagas']
I am writing a research paper on a idea Analysis of High-throughput DNA Methylation Bead Arrays Utilizing Bayesian Genotyping Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Analysis of High-throughput DNA Methylation Bead Arrays Utilizing Bayesian Genotyping Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a statistical framework, MAMS-M, for determining the methylation status of hundreds of cancer related CpG sites. MAMS-M extends and adapts our previous SNP genotyping algorithm, MAMS, to methylation bead array data, exploiting the similarities in data structure beten the two platforms. MAMS-M employs a multi-site, multi-array model-based clustering approach to derive initial methylation calls, and then recalibrate these calls and associated confidence measures using site-specific adjustments.  demonstrate the performance of MAMS-M using a real-life data set with cancer applications."}]