THE EFFECTS OF ORDERED-SERIES-OF-MOTIFS ANCHORING AND SUB-CLASS MODELING ON THE GENERATION OF HMMs REPRESENTING HIGHLY DIVERGENT PROTEIN SEQUENCES
Moyra McClure, J. KOWALSKI · 1998
Hidden Markov Models (HMMs) provide a flexible method for representing protein sequence data. Highly divergent data require a more complex approach to HMM generation than previously demonstrated. We describe a strategy of motif anchoring and sub-class modeling that aids in the construction of more informative HMMs as determined by a new algorithm called a stability measure.