Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
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Sequencing of peptides by tandem mass spectrometry has matured to the key technology for proteomics. Noise in the measurement process strongly favors statistical models like NovoHMM, a recently published generative approach based on factorial hidden Markov models [1,2]. We extend this hidden Markov model to include information of doubly charged ions since the original model can only cope with singly charged ions. This modification requires a refined discretization of the mass scale and, thereby, it increases its sensitivity and recall performance on a number of datasets to compare favorably with alternative approaches for mass spectra interpretation.