Tandem mass spectrometry is a powerful and commonly used method to identify protein sequences. Many different scoring functions, needed for a correct identication, have already been proposed in various journals, being based on most diverse approaches. Often, the current knowledge about the important issues in tandem mass spectrometry cannot be considered in the scoring functions. This thesis proposes different, simple approaches to tune existing scoring functions by using some of these ndings. It is shown that only small changes in the scoring functions are required to improve the results signicantly. Two main approaches for improving the results are dealt with in this document. Scoring functions based on naïve Bayesian classiers are tuned and also combined to yield optimization. Even better prospects are achieved with machine learning methods, where it is hoped that a new scoring function, signi cantly better than the existing ones, can be designed.
|Publication status||Published - 2007|
- Scoring Function
- Tandem Mass Spectrometry