Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most tandem mass spectrum identification algorithms use information only from the final spectrum, ignoring precursor information such as peptide retention time (RT). Efforts to exploit peptide RT for peptide identification can be frustrated by its variability across liquid chromatography analyses.  show that peptide RT can be reliably predicted by training a support vector regressor on a single chromatography run. This dynamically trained model outperforms a published statically trained model of peptide RT across diverse chromatography conditions. In addition, the model can be used to filter peptide identifications that produce large discrepancies beten observed and predicted RT. After filtering, estimated true positive peptide identifications increase by as much as 50% at a false discovery rate of 3%, with the largest increase for non-specific cleavage with elastase.
['Aaron A. Klammer', 'Xianhua Yi', 'Michael J. MacCoss', 'William Stafford Noble']
I am writing a research paper on a idea Peptide retention time prediction yields improved tandem mass spectrum identification for diverse chromatography conditions
[{"from": "human", "value": "I am writing a research paper on a idea Peptide retention time prediction yields improved tandem mass spectrum identification for diverse chromatography conditions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most tandem mass spectrum identification algorithms use information only from the final spectrum, ignoring precursor information such as peptide retention time (RT). Efforts to exploit peptide RT for peptide identification can be frustrated by its variability across liquid chromatography analyses.  show that peptide RT can be reliably predicted by training a support vector regressor on a single chromatography run. This dynamically trained model outperforms a published statically trained model of peptide RT across diverse chromatography conditions. In addition, the model can be used to filter peptide identifications that produce large discrepancies beten observed and predicted RT. After filtering, estimated true positive peptide identifications increase by as much as 50% at a false discovery rate of 3%, with the largest increase for non-specific cleavage with elastase."}]