A novel, fast, HMM-with-Duration implementation - for application with a new, pattern recognition informed, nanopore detector

Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions – while the regular HMM will impose a best-fit geometric distribution in its modeling/ representation. Results: A Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extrac...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Stephen Winters-Hilt, Carl Baribault
• Keywords: BMCBI 2007, Nanopore Detector, Regular Hmm, Standard Hmm