A bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering

@inproceedings{Ramoni2005ABF,
  title={A bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering},
  author={Marco F. Ramoni and Isaac S. Kohane and Gil Alterovitz},
  year={2005}
}
Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as Surface-Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian… CONTINUE READING

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